first commit

.gitignore

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+/experiments
+/__pycache__

Readme.md

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+# Next Best View for Reconstruction
+
+## 1. Setup Environment
+### 1.1 Install Main Project
+```bash
+mkdir nbv_rec
+cd nbv_rec
+git clone https://git.hofee.top/hofee/nbv_reconstruction.git
+```
+### 1.2 Install PytorchBoot
+the environment is based on PytorchBoot, clone and install it from [PytorchBoot](https://git.hofee.top/hofee/PyTorchBoot.git)
+```bash
+git clone https://git.hofee.top/hofee/PyTorchBoot.git
+cd PyTorchBoot
+pip install .
+cd ..
+```
+### 1.3 Install Blender (Optional)
+If you want to render your own dataset as described in [section 2. Render Datasets](#2-render-datasets), you'll need to install Blender version 4.0 from [Blender Release](https://download.blender.org/release/Blender4.0/). Here is an example of installing Blender on Ubuntu:
+```bash
+wget https://download.blender.org/release/Blender4.0/blender-4.0.2-linux-x64.tar.xz
+tar -xvf blender-4.0.2-linux-x64.tar.xz
+```
+If blender is not in your PATH, you can add it by:
+```bash
+export PATH=$PATH:/path/to/blender/blender-4.0.2-linux-x64
+```
+To run the blender script, you need to install the `pyyaml` and `scipy` package into your blender python environment. Run the following command to print the python path of your blender:
+```bash
+./blender -b --python-expr "import sys; print(sys.executable)"
+```
+Then copy the python path `/path/to/blender_python` shown in the output and run the following command to install the packages:
+```bash
+/path/to/blender_python -m pip install pyyaml scipy
+```
+### 1.4 Install Blender Render Script (Optional)
+Clone the script from [nbv_rec_blender_render](https://git.hofee.top/hofee/nbv_rec_blender_render.git) and rename it to `blender`:
+```bash
+git clone https://git.hofee.top/hofee/nbv_rec_blender_render.git
+mv nbv_rec_blender_render blender
+```
+
+### 1.5 Check Dependencies
+Switch to the project root directory and run `pytorch-boot scan` or `ptb scan` to check if all dependencies are installed:
+```bash
+cd nbv_reconstruction
+pytorch-boot scan
+# or
+ptb scan
+```
+If you see project structure information in the output, it means all dependencies are correctly installed. Otherwise, you may need to run `pip install xxx` to install the missing packages.
+
+## 2. Render Datasets (Optional)
+### 2.1 Download Object Mesh Models
+Download the mesh models divided into three parts from: 
+ - [object_meshes_part1.zip](None)
+ - [object_meshes_part2.zip](https://pan.baidu.com/s/1pBPhrFtBwEGp1g4vwsLIxA?pwd=1234)
+ - [object_meshes_part3.zip](https://pan.baidu.com/s/1peE8HqFFL0qNFhM5OC69gA?pwd=1234)
+
+or download the whole dataset from [object_meshes.zip](https://pan.baidu.com/s/1ilWWgzg_l7_pPBv64eSgzA?pwd=1234)
+
+Download the table model from [table.obj](https://pan.baidu.com/s/1sjjiID25Es_kmcdUIjU_Dw?pwd=1234)
+
+### 2.2 Set Render Configurations
+Open file `configs/local/view_generate_config.yaml` and modify the parameters to fit your needs. You are required to at least set the following parameters in `runner-generate`:
+ - `object_dir`: the directory of the downloaded object mesh models
+ - `output_dir`: the directory to save the rendered dataset
+ - `table_model_path`: the path of the downloaded table model
+
+### 2.3 Render Dataset
+
+There are two ways to render the dataset:
+
+#### 2.3.1 Render with Visual Monitoring
+
+If you want to visually monitor the rendering progress and machine resource usage:
+
+1. In the terminal, run:
+   ```
+   ptb ui
+   ```
+2. Open your browser and visit http://localhost:5000
+3. Navigate to `Project Dashboard - Project Structure - Applications - generate_view`
+4. Click the `Run` button to execute the rendering script
+
+#### 2.3.2 Render in Terminal
+
+If you don't need visual monitoring and prefer to run the rendering process directly in the terminal, simply run:
+
+```
+ptb run generate_view
+```
+
+This command will start the rendering process without launching the UI.
+
+## 3. Preprocess
+
+⚠️ The preprocessing code is currently not managed by `PytorchBoot`. To run the preprocessing:
+
+1. Open the `./preprocess/preprocessor.py` file.
+2. Locate the `if __name__ == "__main__":` block at the bottom of the file.
+3. Specify the dataset folder by setting `root = "path/to/your/dataset"`.
+4. Run the preprocessing script directly:
+
+   ```
+   python ./preprocess/preprocessor.py
+   ```
+
+This will preprocess the data in the specified dataset folder.
+
+## 4. Generate Strategy Label
+
+### 4.1 Set Configuration
+
+Open the file `configs/local/strategy_generate_config.yaml` and modify the parameters to fit your needs. You are required to at least set the following parameter:
+
+- `datasets.OmniObject3d.root_dir`: the directory of your dataset
+
+### 4.2 Generate Strategy Label
+
+There are two ways to generate the strategy label:
+
+#### 4.2.1 Generate with Visual Monitoring
+
+If you want to visually monitor the generation progress and machine resource usage:
+
+1. In the terminal, run:
+   ```
+   ptb ui
+   ```
+2. Open your browser and visit http://localhost:5000
+3. Navigate to Project Dashboard - Project Structure - Applications - generate_strategy
+4. Click the `Run` button to execute the generation script
+
+#### 4.2.2 Generate in Terminal
+
+If you don't need visual monitoring and prefer to run the generation process directly in the terminal, simply run:
+
+```
+ptb run generate_strategy
+```
+
+This command will start the strategy label generation process without launching the UI.
+
+## 5. Train
+
+### 5.1 Set Configuration
+
+Open the file `configs/local/train_config.yaml` and modify the parameters to fit your needs. You are required to at least set the following parameters in the `experiment` section:
+
+```yaml
+experiment:
+  name: your_experiment_name
+  root_dir: path/to/your/experiment_dir
+  use_checkpoint: False # if True, the checkpoint will be loaded
+  epoch: 600 # specific epoch to load, -1 stands for last epoch
+  max_epochs: 5000 # maximum epochs to train
+  save_checkpoint_interval: 1 # save checkpoint interval
+  test_first: True # if True, test process will be performed before training at each epoch
+```
+
+Adjust these parameters according to your training requirements.
+
+
+### 5.2 Start Training
+
+There are two ways to start the training process:
+
+#### 5.2.1 Train with Visual Monitoring
+
+If you want to visually monitor the training progress and machine resource usage:
+
+1. In the terminal, run:
+   ```
+   ptb ui
+   ```
+2. Open your browser and visit http://localhost:5000
+3. Navigate to Project Dashboard - Project Structure - Applications - train
+4. Click the `Run` button to start the training process
+
+#### 5.2.2 Train in Terminal
+
+If you don't need visual monitoring and prefer to run the training process directly in the terminal, simply run:
+
+```
+ptb run train
+```
+
+This command will start the training process without launching the UI.
+
+## 6. Evaluation
+...

app_generate_strategy.py

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+from PytorchBoot.application import PytorchBootApplication
+from runners.strategy_generator import StrategyGenerator
+
+@PytorchBootApplication("generate_strategy")
+class DataGenerateApp:
+    @staticmethod
+    def start():
+        StrategyGenerator("configs/local/strategy_generate_config.yaml").run()
+        

app_generate_view.py

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+from PytorchBoot.application import PytorchBootApplication
+from runners.view_generator import ViewGenerator
+
+@PytorchBootApplication("generate_view")
+class ViewGenerateApp:
+    @staticmethod
+    def start():
+        ''' 
+            call default or your custom runners here, code will be executed 
+        automatically when type "pytorch-boot run" or "ptb run" in terminal
+        
+            example:
+                Trainer("path_to_your_train_config").run()
+                Evaluator("path_to_your_eval_config").run()
+        '''       
+        ViewGenerator("configs/local/view_generate_config.yaml").run()

app_inference.py

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+from PytorchBoot.application import PytorchBootApplication
+from runners.global_points_inferencer import GlobalPointsInferencer
+from runners.global_and_local_points_inferencer import GlobalAndLocalPointsInferencer
+from runners.local_points_inferencer import LocalPointsInferencer
+from runners.inference_server import InferencerServer
+from runners.evaluate_uncertainty_guide import EvaluateUncertaintyGuide
+@PytorchBootApplication("global_points_inference")
+class GlobalPointsInferenceApp:
+    @staticmethod
+    def start():
+        ''' 
+            call default or your custom runners here, code will be executed 
+        automatically when type "pytorch-boot run" or "ptb run" in terminal
+        
+            example:
+                Trainer("path_to_your_train_config").run()
+                Evaluator("path_to_your_eval_config").run()
+        '''       
+        GlobalPointsInferencer("./configs/local/global_only_inference_config.yaml").run()
+
+@PytorchBootApplication("global_and_local_points_inference")
+class GlobalAndLocalPointsInferenceApp:
+    @staticmethod
+    def start():
+        ''' 
+            call default or your custom runners here, code will be executed 
+        automatically when type "pytorch-boot run" or "ptb run" in terminal
+        
+            example:
+                Trainer("path_to_your_train_config").run()
+                Evaluator("path_to_your_eval_config").run()
+        '''       
+        GlobalAndLocalPointsInferencer("./configs/local/global_pts_and_local_pts_pose.yaml").run()
+
+@PytorchBootApplication("local_points_inference")
+class LocalPointsInferenceApp:
+    @staticmethod
+    def start():
+        ''' 
+            call default or your custom runners here, code will be executed 
+        automatically when type "pytorch-boot run" or "ptb run" in terminal
+        
+            example:
+                Trainer("path_to_your_train_config").run()
+                Evaluator("path_to_your_eval_config").run()
+        '''       
+        LocalPointsInferencer("./configs/local/local_only_inference_config.yaml").run()
+
+@PytorchBootApplication("server")
+class InferenceServerApp:
+    @staticmethod
+    def start():
+        ''' 
+            call default or your custom runners here, code will be executed 
+        automatically when type "pytorch-boot run" or "ptb run" in terminal
+        
+            example:
+                Trainer("path_to_your_train_config").run()
+                Evaluator("path_to_your_eval_config").run()
+        '''       
+        InferencerServer("./configs/server/server_inference_server_config.yaml").run()
+
+@PytorchBootApplication("evaluate_uncertainty_guide")
+class EvaluateUncertaintyGuideApp:
+    @staticmethod
+    def start():
+        ''' 
+            call default or your custom runners here, code will be executed 
+        automatically when type "pytorch-boot run" or "ptb run" in terminal
+        
+            example:
+                Trainer("path_to_your_train_config").run()
+                Evaluator("path_to_your_eval_config").run()
+        '''       
+        EvaluateUncertaintyGuide("./configs/local/uncertainty_guide_evaluation_config.yaml").run()

app_sim.py

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+from PytorchBoot.application import PytorchBootApplication
+from runners.simulator import Simulator
+
+@PytorchBootApplication("sim")
+class SimulateApp:
+    @staticmethod
+    def start():
+        simulator = Simulator("configs/local/simulation_config.yaml")
+        simulator.run("create")
+        simulator.run("simulate")
+        

app_split.py

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+from PytorchBoot.application import PytorchBootApplication
+from runners.data_spliter import DataSpliter
+
+@PytorchBootApplication("split_data")
+class DataSplitApp:
+    @staticmethod
+    def start():
+        DataSpliter("configs/server/server_split_dataset_config.yaml").run()
+        

app_train.py

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+from PytorchBoot.application import PytorchBootApplication
+from PytorchBoot.runners.trainer import DefaultTrainer
+
+@PytorchBootApplication("train")
+class TrainApp:
+    @staticmethod
+    def start():
+        DefaultTrainer("configs/server/server_train_config.yaml").run()

beans/predict_result.py

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+import numpy as np
+from sklearn.cluster import DBSCAN
+
+class PredictResult:
+    def __init__(self, raw_predict_result, input_pts=None, cluster_params=dict(eps=0.5, min_samples=2)):
+        self.input_pts = input_pts
+        self.cluster_params = cluster_params
+        self.sampled_9d_pose = raw_predict_result
+        self.sampled_matrix_pose = self.get_sampled_matrix_pose()
+        self.distance_matrix = self.calculate_distance_matrix()
+        self.clusters = self.get_cluster_result()
+        self.candidate_matrix_poses = self.get_candidate_poses()
+        self.candidate_9d_poses = [np.concatenate((self.matrix_to_rotation_6d_numpy(matrix[:3,:3]), matrix[:3,3].reshape(-1,)), axis=-1) for matrix in self.candidate_matrix_poses]
+        self.cluster_num = len(self.clusters)
+
+    @staticmethod
+    def rotation_6d_to_matrix_numpy(d6):
+        a1, a2 = d6[:3], d6[3:]
+        b1 = a1 / np.linalg.norm(a1)
+        b2 = a2 - np.dot(b1, a2) * b1
+        b2 = b2 / np.linalg.norm(b2)
+        b3 = np.cross(b1, b2)
+        return np.stack((b1, b2, b3), axis=-2)
+    
+    @staticmethod
+    def matrix_to_rotation_6d_numpy(matrix):
+        return np.copy(matrix[:2, :]).reshape((6,))
+    
+    def __str__(self):
+        info = "Predict Result:\n"
+        info += f"  Predicted pose number: {len(self.sampled_9d_pose)}\n"
+        info += f"  Cluster number: {self.cluster_num}\n"
+        for i, cluster in enumerate(self.clusters):
+            info += f"   - Cluster {i} size: {len(cluster)}\n"
+        max_distance = np.max(self.distance_matrix[self.distance_matrix != 0])
+        min_distance = np.min(self.distance_matrix[self.distance_matrix != 0])
+        info += f"  Max distance: {max_distance}\n"
+        info += f"  Min distance: {min_distance}\n"
+        return info
+    
+    def get_sampled_matrix_pose(self):
+        sampled_matrix_pose = []
+        for pose in self.sampled_9d_pose:
+            rotation = pose[:6]
+            translation = pose[6:]
+            pose = self.rotation_6d_to_matrix_numpy(rotation)
+            pose = np.concatenate((pose, translation.reshape(-1, 1)), axis=-1)
+            pose = np.concatenate((pose, np.array([[0, 0, 0, 1]])), axis=-2)
+            sampled_matrix_pose.append(pose)
+        return np.array(sampled_matrix_pose)
+
+    def rotation_distance(self, R1, R2):
+        R = np.dot(R1.T, R2)
+        trace = np.trace(R)
+        angle = np.arccos(np.clip((trace - 1) / 2, -1, 1))
+        return angle
+
+    def calculate_distance_matrix(self):
+        n = len(self.sampled_matrix_pose)
+        dist_matrix = np.zeros((n, n))
+        for i in range(n):
+            for j in range(n):
+                dist_matrix[i, j] = self.rotation_distance(self.sampled_matrix_pose[i][:3, :3], self.sampled_matrix_pose[j][:3, :3])
+        return dist_matrix
+
+    def cluster_rotations(self):
+        clustering = DBSCAN(eps=self.cluster_params['eps'], min_samples=self.cluster_params['min_samples'], metric='precomputed')
+        labels = clustering.fit_predict(self.distance_matrix)
+        return labels
+
+    def get_cluster_result(self):
+        labels = self.cluster_rotations()
+        cluster_num = len(set(labels)) - (1 if -1 in labels else 0)
+        clusters = []
+        for _ in range(cluster_num):
+            clusters.append([])
+        for matrix_pose, label in zip(self.sampled_matrix_pose, labels):
+            if label != -1:
+                clusters[label].append(matrix_pose)
+        clusters.sort(key=len, reverse=True)
+        return clusters
+
+    def get_center_matrix_pose_from_cluster(self, cluster):
+        min_total_distance = float('inf')
+        center_matrix_pose = None
+        
+        for matrix_pose in cluster:
+            total_distance = 0
+            for other_matrix_pose in cluster:
+                rot_distance = self.rotation_distance(matrix_pose[:3, :3], other_matrix_pose[:3, :3])
+                total_distance += rot_distance
+            
+            if total_distance < min_total_distance:
+                min_total_distance = total_distance
+                center_matrix_pose = matrix_pose
+        
+        return center_matrix_pose
+    
+    def get_candidate_poses(self):
+        candidate_poses = []
+        for cluster in self.clusters:
+            candidate_poses.append(self.get_center_matrix_pose_from_cluster(cluster))
+        return candidate_poses
+    
+    def visualize(self):
+        import plotly.graph_objects as go
+        fig = go.Figure()
+        if self.input_pts is not None:
+            fig.add_trace(go.Scatter3d(
+                x=self.input_pts[:, 0], y=self.input_pts[:, 1], z=self.input_pts[:, 2],
+                mode='markers', marker=dict(size=1, color='gray', opacity=0.5), name='Input Points'
+            ))
+        colors = ['aggrnyl', 'agsunset', 'algae', 'amp', 'armyrose', 'balance',
+             'blackbody', 'bluered', 'blues', 'blugrn', 'bluyl', 'brbg']
+        for i, cluster in enumerate(self.clusters):
+            color = colors[i]
+            candidate_pose = self.candidate_matrix_poses[i]
+            origin_candidate = candidate_pose[:3, 3]
+            z_axis_candidate = candidate_pose[:3, 2]
+            for pose in cluster:
+                origin = pose[:3, 3]
+                z_axis = pose[:3, 2]
+                fig.add_trace(go.Cone(
+                    x=[origin[0]], y=[origin[1]], z=[origin[2]],
+                    u=[z_axis[0]], v=[z_axis[1]], w=[z_axis[2]],
+                    colorscale=color,
+                    sizemode="absolute", sizeref=0.05, anchor="tail", showscale=False
+                ))
+            fig.add_trace(go.Cone(
+                x=[origin_candidate[0]], y=[origin_candidate[1]], z=[origin_candidate[2]],
+                u=[z_axis_candidate[0]], v=[z_axis_candidate[1]], w=[z_axis_candidate[2]],
+                colorscale=color,
+                sizemode="absolute", sizeref=0.1, anchor="tail", showscale=False
+            ))
+        
+        fig.update_layout(
+            title="Clustered Poses and Input Points",
+            scene=dict(
+                xaxis_title='X',
+                yaxis_title='Y',
+                zaxis_title='Z'
+            ),
+            margin=dict(l=0, r=0, b=0, t=40),
+            scene_camera=dict(eye=dict(x=1.25, y=1.25, z=1.25))
+        )
+        
+        fig.show()
+
+
+
+if __name__ == "__main__":
+    step = 0
+    raw_predict_result = np.load(f"inference_result_pack/inference_result_pack/{step}/all_pred_pose_9d.npy")
+    input_pts = np.loadtxt(f"inference_result_pack/inference_result_pack/{step}/input_pts.txt")
+    print(raw_predict_result.shape)
+    predict_result = PredictResult(raw_predict_result, input_pts, cluster_params=dict(eps=0.25, min_samples=3))
+    print(predict_result)
+    print(len(predict_result.candidate_matrix_poses))
+    print(predict_result.distance_matrix)
+    #import ipdb; ipdb.set_trace()
+    predict_result.visualize()
+

configs/local/global_only_inference_config.yaml

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+
+runner:
+  general:
+    seed: 0
+    device: cuda
+    cuda_visible_devices: "0,1,2,3,4,5,6,7"
+    
+  experiment:
+    name: ab_global_pts_and_local_pose
+    root_dir: "experiments"
+    epoch: 200 # -1 stands for last epoch
+
+  test:
+    dataset_list:
+      - OmniObject3d_test
+
+  blender_script_path: "/media/hofee/data/project/python/nbv_reconstruction/blender/data_renderer.py"
+  output_dir: "/media/hofee/data/project/exp/new_ab_global_pts_and_local_pose"
+  pipeline: nbv_reconstruction_pipeline_global
+  voxel_size: 0.003
+  min_new_area: 1.0
+dataset:
+  OmniObject3d_test:
+    root_dir: "/media/hofee/repository/final_test_set/preprocessed_dataset"
+    model_dir: "/media/hofee/data/data/target/target_formulated_view"
+    source: seq_reconstruction_dataset_preprocessed
+    type: test
+    filter_degree: 75
+    eval_list:
+      - pose_diff
+      - coverage_rate_increase
+    ratio: 0.1
+    batch_size: 1
+    num_workers: 12
+    pts_num: 8192
+    load_from_preprocess: True
+
+pipeline:
+  nbv_reconstruction_pipeline_local:
+    modules:
+      pts_encoder: pointnet++_encoder
+      seq_encoder: transformer_seq_encoder
+      pose_encoder: pose_encoder
+      view_finder: gf_view_finder
+    eps: 1e-5
+    global_scanned_feat: True
+  nbv_reconstruction_pipeline_global:
+    modules:
+      pts_encoder: pointnet++_encoder
+      seq_encoder: transformer_seq_encoder
+      pose_encoder: pose_encoder
+      view_finder: gf_view_finder
+    eps: 1e-5
+    global_scanned_feat: True
+  nbv_reconstruction_pipeline_local_only:
+    modules:
+      pts_encoder: pointnet++_encoder
+      seq_encoder: transformer_seq_encoder
+      pose_encoder: pose_encoder
+      view_finder: gf_view_finder
+    eps: 1e-5
+    global_scanned_feat: True
+  nbv_reconstruction_pipeline_global_only:
+    modules:
+      pts_encoder: pointnet++_encoder
+      seq_encoder: transformer_seq_encoder
+      pose_encoder: pose_encoder
+      view_finder: gf_view_finder
+    eps: 1e-5
+    global_scanned_feat: True
+  nbv_reconstruction_pipeline_mlp:
+    modules:
+      pts_encoder: pointnet++_encoder
+      seq_encoder: transformer_seq_encoder
+      pose_encoder: pose_encoder
+      view_finder: mlp_view_finder
+    eps: 1e-5
+    global_scanned_feat: True
+
+    
+module:
+
+  pointnet_encoder:
+    in_dim: 3
+    out_dim: 1024
+    global_feat: True
+    feature_transform: False
+
+  pointnet++_encoder:
+    in_dim: 3
+
+  transformer_seq_encoder:
+    embed_dim: 256
+    num_heads: 4
+    ffn_dim: 256
+    num_layers: 3
+    output_dim: 1024
+
+  gf_view_finder:
+    t_feat_dim: 128
+    pose_feat_dim: 256
+    main_feat_dim: 2048
+    regression_head: Rx_Ry_and_T
+    pose_mode: rot_matrix
+    per_point_feature: False
+    sample_mode: ode
+    sampling_steps: 500
+    sde_mode: ve
+
+  pose_encoder:
+    pose_dim: 9
+    out_dim: 256
+
+  pts_num_encoder:
+    out_dim: 64

configs/local/global_points_and_pose_inference_config.yaml

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+runner:
+  general:
+    seed: 0
+    device: cuda
+    cuda_visible_devices: "0,1,2,3,4,5,6,7"
+    
+  experiment:
+    name: ab_global_pts_and_local_pose
+    root_dir: "experiments"
+    epoch: 200 # -1 stands for last epoch
+
+  test:
+    dataset_list:
+      - OmniObject3d_test
+
+  blender_script_path: "/media/hofee/data/project/python/nbv_reconstruction/blender/data_renderer.py"
+  output_dir: "/media/hofee/data/project/exp/new_ab_global_only"
+  pipeline: nbv_reconstruction_pipeline_global_only
+  voxel_size: 0.003
+  min_new_area: 1.0
+
+dataset:
+    OmniObject3d_test:
+      source: seq_reconstruction_dataset_preprocessed
+      root_dir: /media/hofee/repository/final_test_set/preprocessed_dataset
+      model_dir: /media/hofee/data/data/target/target_formulated_view
+      type: test
+      filter_degree: 75
+      eval_list:
+        - pose_diff
+        - coverage_rate_increase
+      ratio: 0.1
+      batch_size: 1
+      num_workers: 12
+      pts_num: 8192
+      load_from_preprocess: True
+
+pipeline:
+  nbv_reconstruction_pipeline_local:
+    modules:
+      pts_encoder: pointnet++_encoder
+      seq_encoder: transformer_seq_encoder
+      pose_encoder: pose_encoder
+      view_finder: gf_view_finder
+    eps: 1e-5
+    global_scanned_feat: True
+  nbv_reconstruction_pipeline_global:
+    modules:
+      pts_encoder: pointnet++_encoder
+      seq_encoder: transformer_seq_encoder
+      pose_encoder: pose_encoder
+      view_finder: gf_view_finder
+    eps: 1e-5
+    global_scanned_feat: True
+  nbv_reconstruction_pipeline_local_only:
+    modules:
+      pts_encoder: pointnet++_encoder
+      seq_encoder: transformer_seq_encoder
+      pose_encoder: pose_encoder
+      view_finder: gf_view_finder
+    eps: 1e-5
+    global_scanned_feat: True
+  nbv_reconstruction_pipeline_global_only:
+    modules:
+      pts_encoder: pointnet++_encoder
+      seq_encoder: transformer_seq_encoder
+      pose_encoder: pose_encoder
+      view_finder: gf_view_finder
+    eps: 1e-5
+    global_scanned_feat: True
+  nbv_reconstruction_pipeline_mlp:
+    modules:
+      pts_encoder: pointnet++_encoder
+      seq_encoder: transformer_seq_encoder
+      pose_encoder: pose_encoder
+      view_finder: mlp_view_finder
+    eps: 1e-5
+    global_scanned_feat: True
+
+    
+module:
+
+  pointnet_encoder:
+    in_dim: 3
+    out_dim: 1024
+    global_feat: True
+    feature_transform: False
+
+  pointnet++_encoder:
+    in_dim: 3
+
+  transformer_seq_encoder:
+    embed_dim: 256
+    num_heads: 4
+    ffn_dim: 256
+    num_layers: 3
+    output_dim: 1024
+
+  gf_view_finder:
+    t_feat_dim: 128
+    pose_feat_dim: 256
+    main_feat_dim: 2048
+    regression_head: Rx_Ry_and_T
+    pose_mode: rot_matrix
+    per_point_feature: False
+    sample_mode: ode
+    sampling_steps: 500
+    sde_mode: ve
+
+  pose_encoder:
+    pose_dim: 9
+    out_dim: 256
+
+  pts_num_encoder:
+    out_dim: 64

configs/local/global_pts_and_local_pts_pose.yaml

@@ -0,0 +1,117 @@
+
+runner:
+  general:
+    seed: 0
+    device: cuda
+    cuda_visible_devices: "0,1,2,3,4,5,6,7"
+    
+  experiment:
+    name: ab_global_pts_and_local_pts_pose
+    root_dir: "experiments"
+    epoch: 200 # -1 stands for last epoch
+
+  test:
+    dataset_list:
+      - OmniObject3d_test
+
+  blender_script_path: "/media/hofee/data/project/python/nbv_reconstruction/blender/data_renderer.py"
+  output_dir: "/media/hofee/data/project/exp/new_ab_global_pts_and_local_pts_pose"
+  pipeline: nbv_reconstruction_pipeline_local
+  voxel_size: 0.003
+  min_new_area: 1.0
+dataset:
+  OmniObject3d_test:
+    root_dir: "/media/hofee/repository/final_test_set/preprocessed_dataset"
+    model_dir: "/media/hofee/data/data/target/target_formulated_view"
+    source: seq_reconstruction_dataset_preprocessed
+    # split_file: "C:\\Document\\Datasets\\data_list\\OmniObject3d_test.txt"
+    type: test
+    filter_degree: 75
+    eval_list:
+      - pose_diff
+      - coverage_rate_increase
+    ratio: 0.1
+    batch_size: 1
+    num_workers: 12
+    pts_num: 8192
+    load_from_preprocess: True
+
+pipeline:
+  nbv_reconstruction_pipeline_local:
+    modules:
+      pts_encoder: pointnet++_encoder
+      seq_encoder: transformer_seq_encoder
+      pose_encoder: pose_encoder
+      view_finder: gf_view_finder
+    eps: 1e-5
+    global_scanned_feat: True
+  nbv_reconstruction_pipeline_global:
+    modules:
+      pts_encoder: pointnet++_encoder
+      seq_encoder: transformer_seq_encoder
+      pose_encoder: pose_encoder
+      view_finder: gf_view_finder
+    eps: 1e-5
+    global_scanned_feat: True
+  nbv_reconstruction_pipeline_local_only:
+    modules:
+      pts_encoder: pointnet++_encoder
+      seq_encoder: transformer_seq_encoder
+      pose_encoder: pose_encoder
+      view_finder: gf_view_finder
+    eps: 1e-5
+    global_scanned_feat: True
+  nbv_reconstruction_pipeline_global_only:
+    modules:
+      pts_encoder: pointnet++_encoder
+      seq_encoder: transformer_seq_encoder
+      pose_encoder: pose_encoder
+      view_finder: gf_view_finder
+    eps: 1e-5
+    global_scanned_feat: True
+  nbv_reconstruction_pipeline_mlp:
+    modules:
+      pts_encoder: pointnet++_encoder
+      seq_encoder: transformer_seq_encoder
+      pose_encoder: pose_encoder
+      view_finder: mlp_view_finder
+    eps: 1e-5
+    global_scanned_feat: True
+
+    
+module:
+
+  pointnet_encoder:
+    in_dim: 3
+    out_dim: 1024
+    global_feat: True
+    feature_transform: False
+
+  pointnet++_encoder:
+    in_dim: 3
+
+  transformer_seq_encoder:
+    embed_dim: 1280
+    num_heads: 4
+    ffn_dim: 256
+    num_layers: 3
+    output_dim: 1024
+
+  gf_view_finder:
+    t_feat_dim: 128
+    pose_feat_dim: 256
+    main_feat_dim: 2048
+    regression_head: Rx_Ry_and_T
+    pose_mode: rot_matrix
+    per_point_feature: False
+    sample_mode: ode
+    sampling_steps: 500
+    sde_mode: ve
+
+  pose_encoder:
+    pose_dim: 9
+    out_dim: 256
+    
+
+  pts_num_encoder:
+    out_dim: 64

configs/local/local_only_inference_config.yaml

@@ -0,0 +1,129 @@
+
+runner:
+  general:
+    seed: 0
+    device: cuda
+    cuda_visible_devices: "0,1,2,3,4,5,6,7"
+    
+  experiment:
+    name: ab_local_only
+    root_dir: "experiments"
+    epoch: 200 # -1 stands for last epoch
+
+  test:
+    dataset_list:
+      - OmniObject3d_test
+
+  blender_script_path: "/media/hofee/data/project/python/nbv_reconstruction/blender/data_renderer.py"
+  output_dir: "/media/hofee/data/project/exp/new_ab_local_only"
+  pipeline: nbv_reconstruction_pipeline_local_only
+  voxel_size: 0.003
+  min_new_area: 1.0
+dataset:
+  # OmniObject3d_train:
+  #   root_dir: "C:\\Document\\Datasets\\inference_test1"
+  #   model_dir: "C:\\Document\\Datasets\\scaled_object_meshes"
+  #   source: seq_reconstruction_dataset_preprocessed
+  #   split_file: "C:\\Document\\Datasets\\data_list\\sample.txt"
+  #   type: test
+  #   filter_degree: 75
+  #   ratio: 1
+  #   batch_size: 1
+  #   num_workers: 12
+  #   pts_num: 8192
+  #   load_from_preprocess: True
+
+  OmniObject3d_test:
+    root_dir: "/media/hofee/repository/final_test_set/preprocessed_dataset"
+    model_dir: "/media/hofee/data/data/target/target_formulated_view"
+    source: seq_reconstruction_dataset_preprocessed
+    # split_file: "C:\\Document\\Datasets\\data_list\\OmniObject3d_test.txt"
+    type: test
+    filter_degree: 75
+    eval_list:
+      - pose_diff
+      - coverage_rate_increase
+    ratio: 0.1
+    batch_size: 1
+    num_workers: 12
+    pts_num: 8192
+    load_from_preprocess: True
+
+pipeline:
+  nbv_reconstruction_pipeline_local:
+    modules:
+      pts_encoder: pointnet++_encoder
+      seq_encoder: transformer_seq_encoder
+      pose_encoder: pose_encoder
+      view_finder: gf_view_finder
+    eps: 1e-5
+    global_scanned_feat: True
+  nbv_reconstruction_pipeline_global:
+    modules:
+      pts_encoder: pointnet++_encoder
+      seq_encoder: transformer_seq_encoder
+      pose_encoder: pose_encoder
+      view_finder: gf_view_finder
+    eps: 1e-5
+    global_scanned_feat: True
+  nbv_reconstruction_pipeline_local_only:
+    modules:
+      pts_encoder: pointnet++_encoder
+      seq_encoder: transformer_seq_encoder
+      pose_encoder: pose_encoder
+      view_finder: gf_view_finder
+    eps: 1e-5
+    global_scanned_feat: True
+  nbv_reconstruction_pipeline_global_only:
+    modules:
+      pts_encoder: pointnet++_encoder
+      seq_encoder: transformer_seq_encoder
+      pose_encoder: pose_encoder
+      view_finder: gf_view_finder
+    eps: 1e-5
+    global_scanned_feat: True
+  nbv_reconstruction_pipeline_mlp:
+    modules:
+      pts_encoder: pointnet++_encoder
+      seq_encoder: transformer_seq_encoder
+      pose_encoder: pose_encoder
+      view_finder: mlp_view_finder
+    eps: 1e-5
+    global_scanned_feat: True
+
+    
+module:
+
+  pointnet_encoder:
+    in_dim: 3
+    out_dim: 1024
+    global_feat: True
+    feature_transform: False
+
+  pointnet++_encoder:
+    in_dim: 3
+
+  transformer_seq_encoder:
+    embed_dim: 1280
+    num_heads: 4
+    ffn_dim: 256
+    num_layers: 3
+    output_dim: 1024
+
+  gf_view_finder:
+    t_feat_dim: 128
+    pose_feat_dim: 256
+    main_feat_dim: 1024
+    regression_head: Rx_Ry_and_T
+    pose_mode: rot_matrix
+    per_point_feature: False
+    sample_mode: ode
+    sampling_steps: 500
+    sde_mode: ve
+
+  pose_encoder:
+    pose_dim: 9
+    out_dim: 256
+
+  pts_num_encoder:
+    out_dim: 64

configs/local/simulation_config.yaml

@@ -0,0 +1,36 @@
+runner:
+  general:
+    seed: 0
+    device: cuda
+    cuda_visible_devices: "0,1,2,3,4,5,6,7"
+    
+  experiment:
+    name: simulation_debug
+    root_dir: "experiments"
+
+simulation:
+  robot:
+    urdf_path: "assets/franka_panda/panda.urdf"
+    initial_position: [0, 0, 0]  # 机械臂基座位置
+    initial_orientation: [0, 0, 0]  # 机械臂基座朝向(欧拉角)
+  
+  turntable:
+    radius: 0.3  # 转盘半径(米)
+    height: 0.1  # 转盘高度
+    center_position: [0.8, 0, 0.4]  
+  
+  target:
+    obj_dir: /media/hofee/data/project/python/nbv_reconstruction/nbv_reconstruction/assets/object_meshes
+    obj_name: "google_scan-box_0185"        
+    scale: 1.0  # 缩放系数
+    mass: 0.1   # 质量(kg)
+    rgba_color: [0.8, 0.8, 0.8, 1.0]  # 目标物体颜色
+  
+  camera:
+    width: 640
+    height: 480
+    fov: 40
+    near: 0.01
+    far: 5.0
+
+displaytable:

configs/local/split_dataset_config.yaml

@@ -0,0 +1,22 @@
+
+runner:
+  general:
+    seed: 0
+    device: cpu
+    cuda_visible_devices: "0,1,2,3,4,5,6,7"
+    
+  experiment:
+    name: debug
+    root_dir: "experiments"
+  
+  split:
+    root_dir: "/media/hofee/data/project/python/nbv_reconstruction/sample_for_training/scenes"
+    type: "unseen_instance" # "unseen_category"
+    datasets:
+      OmniObject3d_train: 
+        path: "/media/hofee/data/project/python/nbv_reconstruction/sample_for_training/OmniObject3d_train.txt"
+        ratio: 0.9
+
+      OmniObject3d_test: 
+        path: "/media/hofee/data/project/python/nbv_reconstruction/sample_for_training/OmniObject3d_test.txt"
+        ratio: 0.1

configs/local/strategy_generate_config.yaml

@@ -0,0 +1,27 @@
+
+runner:
+  general:
+    seed: 0
+    device: cpu
+    cuda_visible_devices: "0,1,2,3,4,5,6,7"
+
+    
+  experiment:
+    name: debug
+    root_dir: "experiments"
+      
+  generate:
+    voxel_threshold: 0.003
+    overlap_area_threshold: 30
+    compute_with_normal: False
+    scan_points_threshold: 10
+    overwrite: False  
+    seq_num: 10
+    dataset_list:
+      - OmniObject3d
+  
+datasets:
+    OmniObject3d:
+      root_dir: /media/hofee/data/data/test_bottle/view 
+      from: 0
+      to: -1 # ..-1 means end

configs/local/train_config.yaml

@@ -0,0 +1,105 @@
+
+runner:
+  general:
+    seed: 1
+    device: cuda
+    cuda_visible_devices: "0,1,2,3,4,5,6,7"
+    parallel: False
+    
+  experiment:
+    name: debug
+    root_dir: "experiments"
+    use_checkpoint: False
+    epoch: 600 # -1 stands for last epoch
+    max_epochs: 5000
+    save_checkpoint_interval: 1
+    test_first: True  
+
+  train:
+    optimizer:
+      type: Adam
+      lr: 0.0001
+    losses: 
+      - gf_loss
+    dataset: OmniObject3d_train
+  test:
+    frequency: 3 # test frequency
+    dataset_list:
+      - OmniObject3d_test
+
+  pipeline: nbv_reconstruction_pipeline
+  
+dataset:
+  OmniObject3d_train:
+    root_dir: "/media/hofee/data/project/python/nbv_reconstruction/sample_for_training/scenes"
+    model_dir: "/media/hofee/data/data/scaled_object_meshes"
+    source: nbv_reconstruction_dataset
+    split_file: "/media/hofee/data/project/python/nbv_reconstruction/sample_for_training/OmniObject3d_train.txt"
+    type: train
+    ratio: 1
+    batch_size: 1
+    num_workers: 12
+    pts_num: 4096
+    load_from_preprocess: True
+
+  OmniObject3d_test:
+    root_dir: "/media/hofee/data/project/python/nbv_reconstruction/sample_for_training/scenes"
+    model_dir: "/media/hofee/data/data/scaled_object_meshes"
+    source: nbv_reconstruction_dataset
+    split_file: "/media/hofee/data/project/python/nbv_reconstruction/sample_for_training/OmniObject3d_train.txt"
+    type: test
+    filter_degree: 75
+    eval_list:
+      - pose_diff
+      - coverage_rate_increase
+    ratio: 0.1
+    batch_size: 1
+    num_workers: 12
+    pts_num: 4096
+    load_from_preprocess: True
+
+pipeline:
+  nbv_reconstruction_pipeline:
+    pts_encoder: pointnet_encoder
+    seq_encoder: transformer_seq_encoder
+    pose_encoder: pose_encoder
+    view_finder: gf_view_finder
+    
+module:
+
+  pointnet_encoder:
+    in_dim: 3
+    out_dim: 1024
+    global_feat: True
+    feature_transform: False
+
+  transformer_seq_encoder:
+    pts_embed_dim: 1024
+    pose_embed_dim: 256
+    num_heads: 4
+    ffn_dim: 256
+    num_layers: 3
+    output_dim: 2048
+
+  gf_view_finder:
+    t_feat_dim: 128
+    pose_feat_dim: 256
+    main_feat_dim: 3072
+    regression_head: Rx_Ry_and_T
+    pose_mode: rot_matrix
+    per_point_feature: False
+    sample_mode: ode
+    sampling_steps: 500
+    sde_mode: ve
+
+  pose_encoder:
+    pose_dim: 9
+    out_dim: 256
+
+loss_function:
+  gf_loss:
+
+evaluation_method:
+  pose_diff:
+  coverage_rate_increase: 
+    renderer_path: "/media/hofee/data/project/python/nbv_reconstruction/blender/data_renderer.py" 

configs/local/uncertainty_guide_evaluation_config.yaml

@@ -0,0 +1,130 @@
+
+runner:
+  general:
+    seed: 0
+    device: cuda
+    cuda_visible_devices: "0,1,2,3,4,5,6,7"
+    
+  experiment:
+    name: uncertainty_guide_evaluation
+    root_dir: "experiments"
+    epoch: 200 # -1 stands for last epoch
+
+  test:
+    dataset_list:
+      - OmniObject3d_test
+
+  blender_script_path: "/media/hofee/data/project/python/nbv_reconstruction/blender/data_renderer.py"
+  output_dir: "/media/hofee/data/project/exp/old_uncertainty_guide_evaluation"
+  output_data_root: "/media/hofee/repository/code/nbv_rec_uncertainty_guide/output/reconstruction"
+  pipeline: nbv_reconstruction_pipeline_global_only
+  voxel_size: 0.003
+  min_new_area: 1.0
+dataset:
+  # OmniObject3d_train:
+  #   root_dir: "C:\\Document\\Datasets\\inference_test1"
+  #   model_dir: "C:\\Document\\Datasets\\scaled_object_meshes"
+  #   source: seq_reconstruction_dataset_preprocessed
+  #   split_file: "C:\\Document\\Datasets\\data_list\\sample.txt"
+  #   type: test
+  #   filter_degree: 75
+  #   ratio: 1
+  #   batch_size: 1
+  #   num_workers: 12
+  #   pts_num: 8192
+  #   load_from_preprocess: True
+
+  OmniObject3d_test:
+    root_dir: "/media/hofee/data/data/new_testset_output"
+    model_dir: "/media/hofee/data/data/scaled_object_meshes"
+    source: seq_reconstruction_dataset_preprocessed
+    # split_file: "C:\\Document\\Datasets\\data_list\\OmniObject3d_test.txt"
+    type: test
+    filter_degree: 75
+    eval_list:
+      - pose_diff
+      - coverage_rate_increase
+    ratio: 0.1
+    batch_size: 1
+    num_workers: 12
+    pts_num: 8192
+    load_from_preprocess: True
+
+pipeline:
+  nbv_reconstruction_pipeline_local:
+    modules:
+      pts_encoder: pointnet++_encoder
+      seq_encoder: transformer_seq_encoder
+      pose_encoder: pose_encoder
+      view_finder: gf_view_finder
+    eps: 1e-5
+    global_scanned_feat: True
+  nbv_reconstruction_pipeline_global:
+    modules:
+      pts_encoder: pointnet++_encoder
+      seq_encoder: transformer_seq_encoder
+      pose_encoder: pose_encoder
+      view_finder: gf_view_finder
+    eps: 1e-5
+    global_scanned_feat: True
+  nbv_reconstruction_pipeline_local_only:
+    modules:
+      pts_encoder: pointnet++_encoder
+      seq_encoder: transformer_seq_encoder
+      pose_encoder: pose_encoder
+      view_finder: gf_view_finder
+    eps: 1e-5
+    global_scanned_feat: True
+  nbv_reconstruction_pipeline_global_only:
+    modules:
+      pts_encoder: pointnet++_encoder
+      seq_encoder: transformer_seq_encoder
+      pose_encoder: pose_encoder
+      view_finder: gf_view_finder
+    eps: 1e-5
+    global_scanned_feat: True
+  nbv_reconstruction_pipeline_mlp:
+    modules:
+      pts_encoder: pointnet++_encoder
+      seq_encoder: transformer_seq_encoder
+      pose_encoder: pose_encoder
+      view_finder: mlp_view_finder
+    eps: 1e-5
+    global_scanned_feat: True
+
+    
+module:
+
+  pointnet_encoder:
+    in_dim: 3
+    out_dim: 1024
+    global_feat: True
+    feature_transform: False
+
+  pointnet++_encoder:
+    in_dim: 3
+
+  transformer_seq_encoder:
+    embed_dim: 256
+    num_heads: 4
+    ffn_dim: 256
+    num_layers: 3
+    output_dim: 1024
+
+  gf_view_finder:
+    t_feat_dim: 128
+    pose_feat_dim: 256
+    main_feat_dim: 2048
+    regression_head: Rx_Ry_and_T
+    pose_mode: rot_matrix
+    per_point_feature: False
+    sample_mode: ode
+    sampling_steps: 500
+    sde_mode: ve
+
+  pose_encoder:
+    pose_dim: 9
+    out_dim: 256
+
+  pts_num_encoder:
+    out_dim: 64

configs/local/view_generate_config.yaml

@@ -0,0 +1,52 @@
+runner:
+  general:
+    seed: 0
+    device: cpu
+    cuda_visible_devices: 0,1,2,3,4,5,6,7
+  experiment:
+    name: debug
+    root_dir: experiments
+  generate:
+    port: 5002
+    from: 0
+    to: 50 # -1 means all
+    object_dir: /media/hofee/data/data/test_bottle/bottle_mesh
+    table_model_path: /media/hofee/data/data/others/table.obj
+    output_dir: /media/hofee/data/data/test_bottle/view
+    binocular_vision: true
+    plane_size: 10
+    max_views: 512
+    min_views: 128
+    random_view_ratio: 0.002
+    min_cam_table_included_degree: 20
+    max_diag: 0.7
+    min_diag: 0.01
+    random_config:
+      display_table:
+        min_height: 0.05
+        max_height: 0.15
+        min_radius: 0.2
+        max_radius: 0.3
+      display_object:
+        min_x: 0
+        max_x: 0.05
+        min_y: 0
+        max_y: 0.05
+        min_z: 0.01
+        max_z: 0.01
+        random_rotation_ratio: 0.0
+      random_objects:
+        num: 4
+        cluster: 0.9
+    light_and_camera_config:
+      Camera:
+        near_plane: 0.01
+        far_plane: 5
+        fov_vertical: 25
+        resolution: [640,400]
+        eye_distance: 0.10
+        eye_angle: 25
+      Light:
+        location: [0,0,3.5]
+        orientation: [0,0,0]
+        power: 150

configs/server/server_inference_config.yaml

@@ -0,0 +1,92 @@
+
+runner:
+  general:
+    seed: 1
+    device: cuda
+    cuda_visible_devices: "0,1,2,3,4,5,6,7"
+    
+  experiment:
+    name: w_gf_wo_lf_full_debug
+    root_dir: "experiments"
+    epoch: 1 # -1 stands for last epoch
+
+  test:
+    dataset_list:
+      - OmniObject3d_train
+
+  blender_script_path: ""
+  output_dir: ""
+  pipeline: nbv_reconstruction_global_pts_pipeline
+  
+dataset:
+  OmniObject3d_train:
+    root_dir: "/home/data/hofee/project/nbv_rec/data/nbv_rec_data_512_preproc_npy"
+    model_dir: "/home/data/hofee/project/nbv_rec/data/scaled_object_meshes" 
+    source: seq_nbv_reconstruction_dataset 
+    split_file: "/home/data/hofee/project/nbv_rec/data/OmniObject3d_train.txt" 
+    type: test
+    filter_degree: 75
+    ratio: 1
+    batch_size: 1
+    num_workers: 12
+    pts_num: 4096
+    load_from_preprocess: True
+
+pipeline:
+  nbv_reconstruction_local_pts_pipeline:
+    modules:
+      pts_encoder: pointnet_encoder
+      seq_encoder: transformer_seq_encoder
+      pose_encoder: pose_encoder
+      view_finder: gf_view_finder
+    eps: 1e-5
+    global_scanned_feat: False
+
+  nbv_reconstruction_global_pts_pipeline:
+    modules:
+      pts_encoder: pointnet_encoder
+      pose_seq_encoder: transformer_pose_seq_encoder
+      pose_encoder: pose_encoder
+      view_finder: gf_view_finder
+    eps: 1e-5
+    global_scanned_feat: True
+
+
+    
+module:
+
+  pointnet_encoder:
+    in_dim: 3
+    out_dim: 1024
+    global_feat: True
+    feature_transform: False
+
+  transformer_seq_encoder:
+    pts_embed_dim: 1024
+    pose_embed_dim: 256
+    num_heads: 4
+    ffn_dim: 256
+    num_layers: 3
+    output_dim: 2048
+
+  transformer_pose_seq_encoder:
+    pose_embed_dim: 256
+    num_heads: 4
+    ffn_dim: 256
+    num_layers: 3
+    output_dim: 1024
+
+  gf_view_finder:
+    t_feat_dim: 128
+    pose_feat_dim: 256
+    main_feat_dim: 2048
+    regression_head: Rx_Ry_and_T
+    pose_mode: rot_matrix
+    per_point_feature: False
+    sample_mode: ode
+    sampling_steps: 500
+    sde_mode: ve
+
+  pose_encoder:
+    pose_dim: 9
+    out_dim: 256

configs/server/server_inference_server_config.yaml

@@ -0,0 +1,53 @@
+
+runner:
+  general:
+    seed: 0
+    device: cuda
+    cuda_visible_devices: "0,1,2,3,4,5,6,7"
+    
+  experiment:
+    name: train_ab_global_only
+    root_dir: "experiments"
+    epoch: -1 # -1 stands for last epoch
+
+  pipeline: nbv_reconstruction_pipeline
+  voxel_size: 0.003
+
+pipeline:
+  nbv_reconstruction_pipeline:
+    modules:
+      pts_encoder: pointnet_encoder
+      seq_encoder: transformer_seq_encoder
+      pose_encoder: pose_encoder
+      view_finder: gf_view_finder
+    eps: 1e-5
+    global_scanned_feat: True
+
+module:
+  pointnet_encoder:
+    in_dim: 3
+    out_dim: 1024
+    global_feat: True
+    feature_transform: False
+  transformer_seq_encoder:
+    embed_dim: 256
+    num_heads: 4
+    ffn_dim: 256
+    num_layers: 3
+    output_dim: 1024
+
+  gf_view_finder:
+    t_feat_dim: 128
+    pose_feat_dim: 256
+    main_feat_dim: 2048
+    regression_head: Rx_Ry_and_T
+    pose_mode: rot_matrix
+    per_point_feature: False
+    sample_mode: ode
+    sampling_steps: 500
+    sde_mode: ve
+  pose_encoder:
+    pose_dim: 9
+    out_dim: 256
+  pts_num_encoder:
+    out_dim: 64

configs/server/server_split_dataset_config.yaml

@@ -0,0 +1,22 @@
+
+runner:
+  general:
+    seed: 0
+    device: cpu
+    cuda_visible_devices: "0,1,2,3,4,5,6,7"
+    
+  experiment:
+    name: debug
+    root_dir: "experiments"
+  
+  split: # 
+    root_dir: "/data/hofee/data/packed_preprocessed_data"
+    type: "unseen_instance" # "unseen_category"
+    datasets:
+      OmniObject3d_train: 
+        path: "/data/hofee/data/OmniObject3d_train.txt"
+        ratio: 0.9
+
+      OmniObject3d_test: 
+        path: "/data/hofee/data/OmniObject3d_test.txt"
+        ratio: 0.1

configs/server/server_train_config.yaml

@@ -0,0 +1,167 @@
+
+runner:
+  general:
+    seed: 0
+    device: cuda
+    cuda_visible_devices: "2"
+    parallel: False
+    
+  experiment:
+    name: newtrain_mlp
+    root_dir: "experiments"
+    use_checkpoint: False
+    epoch: -1 # -1 stands for last epoch
+    max_epochs: 5000
+    save_checkpoint_interval: 1
+    test_first: False  
+
+  train:
+    optimizer:
+      type: Adam
+      lr: 0.0001
+    losses:   
+      - mse_loss
+    dataset: OmniObject3d_train
+  test:
+    frequency: 3 # test frequency
+    dataset_list:
+      - OmniObject3d_test
+      - OmniObject3d_val
+
+  pipeline: nbv_reconstruction_pipeline_mlp
+  
+dataset:
+  OmniObject3d_train:
+    root_dir: "/data/hofee/data/new_full_data"
+    model_dir: "../data/scaled_object_meshes"
+    source: nbv_reconstruction_dataset
+    split_file: "/data/hofee/data/new_full_data_list/new_OmniObject3d_train.txt"
+    type: train
+    cache: True
+    ratio: 1
+    batch_size: 24
+    num_workers: 128
+    pts_num: 8192
+    load_from_preprocess: True
+
+  OmniObject3d_test:
+    root_dir: "/data/hofee/data/new_full_data"
+    model_dir: "../data/scaled_object_meshes"
+    source: nbv_reconstruction_dataset
+    split_file: "/data/hofee/data/new_full_data_list/new_OmniObject3d_test.txt"
+    type: test
+    cache: True
+    filter_degree: 75
+    eval_list:
+      - pose_diff
+    ratio: 1
+    batch_size: 32
+    num_workers: 12
+    pts_num: 8192
+    load_from_preprocess: True
+
+  OmniObject3d_val:
+    root_dir: "/data/hofee/data/new_full_data"
+    model_dir: "../data/scaled_object_meshes"
+    source: nbv_reconstruction_dataset
+    split_file: "/data/hofee/data/new_full_data_list/new_OmniObject3d_train.txt"
+    type: test
+    cache: True
+    filter_degree: 75
+    eval_list:
+      - pose_diff
+    ratio: 0.1
+    batch_size: 32
+    num_workers: 12
+    pts_num: 8192
+    load_from_preprocess: True
+
+
+pipeline:
+  nbv_reconstruction_pipeline_local:
+    modules:
+      pts_encoder: pointnet++_encoder
+      seq_encoder: transformer_seq_encoder
+      pose_encoder: pose_encoder
+      view_finder: gf_view_finder
+    eps: 1e-5
+    global_scanned_feat: True
+  nbv_reconstruction_pipeline_global:
+    modules:
+      pts_encoder: pointnet++_encoder
+      seq_encoder: transformer_seq_encoder
+      pose_encoder: pose_encoder
+      view_finder: gf_view_finder
+    eps: 1e-5
+    global_scanned_feat: True
+  nbv_reconstruction_pipeline_local_only:
+    modules:
+      pts_encoder: pointnet++_encoder
+      seq_encoder: transformer_seq_encoder
+      pose_encoder: pose_encoder
+      view_finder: gf_view_finder
+    eps: 1e-5
+    global_scanned_feat: True
+  nbv_reconstruction_pipeline_global_only:
+    modules:
+      pts_encoder: pointnet++_encoder
+      seq_encoder: transformer_seq_encoder
+      pose_encoder: pose_encoder
+      view_finder: gf_view_finder
+    eps: 1e-5
+    global_scanned_feat: True
+  nbv_reconstruction_pipeline_mlp:
+    modules:
+      pts_encoder: pointnet++_encoder
+      seq_encoder: transformer_seq_encoder
+      pose_encoder: pose_encoder
+      view_finder: mlp_view_finder
+    eps: 1e-5
+    global_scanned_feat: True
+
+module:
+
+  pointnet_encoder:
+    in_dim: 3
+    out_dim: 1024
+    global_feat: True
+    feature_transform: False
+
+  pointnet++_encoder:
+    in_dim: 3
+
+  transformer_seq_encoder:
+    embed_dim: 1280
+    num_heads: 4
+    ffn_dim: 256
+    num_layers: 3
+    output_dim: 1024
+
+  gf_view_finder:
+    t_feat_dim: 128
+    pose_feat_dim: 256
+    main_feat_dim: 2048
+    regression_head: Rx_Ry_and_T
+    pose_mode: rot_matrix
+    per_point_feature: False
+    sample_mode: ode
+    sampling_steps: 500
+    sde_mode: ve
+
+  pose_encoder:
+    pose_dim: 9
+    out_dim: 256
+    
+
+  pts_num_encoder:
+    out_dim: 64
+
+
+loss_function:
+  gf_loss:
+  mse_loss:
+
+evaluation_method:
+  pose_diff:
+  coverage_rate_increase: 
+    renderer_path: "../blender/data_renderer.py" 

core/ab_global_only_pts_pipeline.py

@@ -0,0 +1,85 @@
+import torch
+from torch import nn
+import PytorchBoot.namespace as namespace
+import PytorchBoot.stereotype as stereotype
+from PytorchBoot.factory.component_factory import ComponentFactory
+from PytorchBoot.utils import Log
+
+
+@stereotype.pipeline("nbv_reconstruction_pipeline_global_only")
+class NBVReconstructionGlobalPointsOnlyPipeline(nn.Module):
+    def __init__(self, config):
+        super(NBVReconstructionGlobalPointsOnlyPipeline, self).__init__()
+        self.config = config
+        self.module_config = config["modules"]
+        self.pts_encoder = ComponentFactory.create(namespace.Stereotype.MODULE, self.module_config["pts_encoder"])
+        self.pose_encoder = ComponentFactory.create(namespace.Stereotype.MODULE, self.module_config["pose_encoder"])
+        self.view_finder = ComponentFactory.create(namespace.Stereotype.MODULE, self.module_config["view_finder"])
+        self.eps = float(self.config["eps"])
+        self.enable_global_scanned_feat = self.config["global_scanned_feat"]
+        
+    def forward(self, data):
+        mode = data["mode"]
+        
+        if mode == namespace.Mode.TRAIN:
+            return self.forward_train(data)
+        elif mode == namespace.Mode.TEST:
+            return self.forward_test(data)
+        else:
+            Log.error("Unknown mode: {}".format(mode), True)
+    
+    def pertube_data(self, gt_delta_9d):
+        bs = gt_delta_9d.shape[0]
+        random_t = torch.rand(bs, device=gt_delta_9d.device) * (1. - self.eps) + self.eps
+        random_t = random_t.unsqueeze(-1)
+        mu, std = self.view_finder.marginal_prob(gt_delta_9d, random_t)
+        std = std.view(-1, 1)
+        z = torch.randn_like(gt_delta_9d)
+        perturbed_x = mu + z * std
+        target_score = - z * std / (std ** 2)
+        return perturbed_x, random_t, target_score, std
+    
+    def forward_train(self, data):
+        main_feat = self.get_main_feat(data)
+        ''' get std '''
+        best_to_world_pose_9d_batch = data["best_to_world_pose_9d"]
+        perturbed_x, random_t, target_score, std = self.pertube_data(best_to_world_pose_9d_batch)
+        input_data = {
+            "sampled_pose": perturbed_x,
+            "t": random_t,
+            "main_feat": main_feat,
+        }
+        estimated_score = self.view_finder(input_data)
+        output = {
+            "estimated_score": estimated_score,
+            "target_score": target_score,
+            "std": std
+        }
+        return output
+    
+    def forward_test(self,data):
+        main_feat = self.get_main_feat(data)
+        repeat_num = data.get("repeat_num", 50)
+        main_feat = main_feat.repeat(repeat_num, 1)
+        estimated_delta_rot_9d, in_process_sample = self.view_finder.next_best_view(
+            main_feat
+        )
+        result = {
+            "pred_pose_9d": estimated_delta_rot_9d,
+            "in_process_sample": in_process_sample,
+        }
+        return result
+    
+    
+    def get_main_feat(self, data):
+        
+        combined_scanned_pts_batch = data['combined_scanned_pts']   
+        global_scanned_feat = self.pts_encoder.encode_points(combined_scanned_pts_batch)
+        main_feat = global_scanned_feat
+        
+        
+        if torch.isnan(main_feat).any():
+            Log.error("nan in main_feat", True)
+        
+        return main_feat
+    

core/ab_local_only_pts_pipeline.py

@@ -0,0 +1,95 @@
+import torch
+from torch import nn
+import PytorchBoot.namespace as namespace
+import PytorchBoot.stereotype as stereotype
+from PytorchBoot.factory.component_factory import ComponentFactory
+from PytorchBoot.utils import Log
+
+@stereotype.pipeline("nbv_reconstruction_pipeline_local_only")
+class NBVReconstructionLocalPointsOnlyPipeline(nn.Module):
+    def __init__(self, config):
+        super(NBVReconstructionLocalPointsOnlyPipeline, self).__init__()
+        self.config = config
+        self.module_config = config["modules"]
+        self.pts_encoder = ComponentFactory.create(namespace.Stereotype.MODULE, self.module_config["pts_encoder"])
+        self.pose_encoder = ComponentFactory.create(namespace.Stereotype.MODULE, self.module_config["pose_encoder"])
+        self.seq_encoder = ComponentFactory.create(namespace.Stereotype.MODULE, self.module_config["seq_encoder"])
+        self.view_finder = ComponentFactory.create(namespace.Stereotype.MODULE, self.module_config["view_finder"])
+        self.eps = float(self.config["eps"])
+        self.enable_global_scanned_feat = self.config["global_scanned_feat"]
+        
+    def forward(self, data):
+        mode = data["mode"]
+        
+        if mode == namespace.Mode.TRAIN:
+            return self.forward_train(data)
+        elif mode == namespace.Mode.TEST:
+            return self.forward_test(data)
+        else:
+            Log.error("Unknown mode: {}".format(mode), True)
+    
+    def pertube_data(self, gt_delta_9d):
+        bs = gt_delta_9d.shape[0]
+        random_t = torch.rand(bs, device=gt_delta_9d.device) * (1. - self.eps) + self.eps
+        random_t = random_t.unsqueeze(-1)
+        mu, std = self.view_finder.marginal_prob(gt_delta_9d, random_t)
+        std = std.view(-1, 1)
+        z = torch.randn_like(gt_delta_9d)
+        perturbed_x = mu + z * std
+        target_score = - z * std / (std ** 2)
+        return perturbed_x, random_t, target_score, std
+    
+    def forward_train(self, data):
+        main_feat = self.get_main_feat(data)
+        ''' get std '''
+        best_to_world_pose_9d_batch = data["best_to_world_pose_9d"]
+        perturbed_x, random_t, target_score, std = self.pertube_data(best_to_world_pose_9d_batch)
+        input_data = {
+            "sampled_pose": perturbed_x,
+            "t": random_t,
+            "main_feat": main_feat,
+        }
+        estimated_score = self.view_finder(input_data)
+        output = {
+            "estimated_score": estimated_score,
+            "target_score": target_score,
+            "std": std
+        }
+        return output
+    
+    def forward_test(self,data):
+        main_feat = self.get_main_feat(data)
+        repeat_num = data.get("repeat_num", 50)
+        main_feat = main_feat.repeat(repeat_num, 1)
+        estimated_delta_rot_9d, in_process_sample = self.view_finder.next_best_view(
+            main_feat
+        )
+        result = {
+            "pred_pose_9d": estimated_delta_rot_9d,
+            "in_process_sample": in_process_sample,
+        }
+        return result
+    
+    
+    def get_main_feat(self, data):
+        scanned_pts_batch = data['scanned_pts']
+        scanned_n_to_world_pose_9d_batch = data['scanned_n_to_world_pose_9d']
+        device = next(self.parameters()).device
+        feat_seq_list = []
+        
+        for scanned_pts,scanned_n_to_world_pose_9d in zip(scanned_pts_batch,scanned_n_to_world_pose_9d_batch):
+            
+            scanned_pts = scanned_pts.to(device)
+            scanned_n_to_world_pose_9d = scanned_n_to_world_pose_9d.to(device)
+            pts_feat = self.pts_encoder.encode_points(scanned_pts)
+            pose_feat = self.pose_encoder.encode_pose(scanned_n_to_world_pose_9d)
+            seq_feat = torch.cat([pts_feat, pose_feat], dim=-1)
+            feat_seq_list.append(seq_feat)
+        main_feat = self.seq_encoder.encode_sequence(feat_seq_list)
+        
+        
+        if torch.isnan(main_feat).any():
+            Log.error("nan in main_feat", True)
+        
+        return main_feat
+    

core/ab_mlp_pipeline.py

@@ -0,0 +1,81 @@
+import torch
+from torch import nn
+import PytorchBoot.namespace as namespace
+import PytorchBoot.stereotype as stereotype
+from PytorchBoot.factory.component_factory import ComponentFactory
+from PytorchBoot.utils import Log
+
+@stereotype.pipeline("nbv_reconstruction_pipeline_mlp")
+class NBVReconstructionMLPPipeline(nn.Module):
+    def __init__(self, config):
+        super(NBVReconstructionMLPPipeline, self).__init__()
+        self.config = config
+        self.module_config = config["modules"]
+        self.pts_encoder = ComponentFactory.create(namespace.Stereotype.MODULE, self.module_config["pts_encoder"])
+        self.pose_encoder = ComponentFactory.create(namespace.Stereotype.MODULE, self.module_config["pose_encoder"])
+        self.seq_encoder = ComponentFactory.create(namespace.Stereotype.MODULE, self.module_config["seq_encoder"])
+        self.view_finder = ComponentFactory.create(namespace.Stereotype.MODULE, self.module_config["view_finder"])
+        self.eps = float(self.config["eps"])
+        self.enable_global_scanned_feat = self.config["global_scanned_feat"]
+        
+    def forward(self, data):
+        mode = data["mode"]
+        
+        if mode == namespace.Mode.TRAIN:
+            return self.forward_train(data)
+        elif mode == namespace.Mode.TEST:
+            return self.forward_test(data)
+        else:
+            Log.error("Unknown mode: {}".format(mode), True)
+    
+    
+    def forward_train(self, data):
+        main_feat = self.get_main_feat(data)
+        ''' get std '''
+        best_to_world_pose_9d_batch = data["best_to_world_pose_9d"]
+        input_data = {
+            "main_feat": main_feat,
+        }
+        pred = self.view_finder(input_data)
+        output = {
+            "pred": pred,
+            "gt": best_to_world_pose_9d_batch,
+        }
+        return output
+    
+    def forward_test(self,data):
+        main_feat = self.get_main_feat(data)
+        estimated_delta_rot_9d, _ = self.view_finder.next_best_view(main_feat)
+        result = {
+            "pred_pose_9d": estimated_delta_rot_9d,
+        }
+        return result
+    
+    
+    def get_main_feat(self, data):
+        scanned_pts_batch = data['scanned_pts']
+        scanned_n_to_world_pose_9d_batch = data['scanned_n_to_world_pose_9d']
+        device = next(self.parameters()).device
+        feat_seq_list = []
+        
+        for scanned_pts,scanned_n_to_world_pose_9d in zip(scanned_pts_batch,scanned_n_to_world_pose_9d_batch):
+            
+            scanned_pts = scanned_pts.to(device)
+            scanned_n_to_world_pose_9d = scanned_n_to_world_pose_9d.to(device)
+            pts_feat = self.pts_encoder.encode_points(scanned_pts)
+            pose_feat = self.pose_encoder.encode_pose(scanned_n_to_world_pose_9d)
+            seq_feat = torch.cat([pts_feat, pose_feat], dim=-1)
+            feat_seq_list.append(seq_feat)
+        main_feat = self.seq_encoder.encode_sequence(feat_seq_list)
+        
+        if self.enable_global_scanned_feat:
+            combined_scanned_pts_batch = data['combined_scanned_pts']   
+            global_scanned_feat = self.pts_encoder.encode_points(combined_scanned_pts_batch)
+            main_feat = torch.cat([main_feat, global_scanned_feat], dim=-1)
+        
+        
+        if torch.isnan(main_feat).any():
+            Log.error("nan in main_feat", True)
+        
+        return main_feat
+    

core/evaluation.py

@@ -0,0 +1,109 @@
+import torch
+import numpy as np
+from utils.reconstruction import ReconstructionUtil
+from utils.pose import PoseUtil
+from utils.pts import PtsUtil
+from utils.render import RenderUtil
+import PytorchBoot.stereotype as stereotype
+import PytorchBoot.namespace as namespace
+from PytorchBoot.utils.log_util import Log
+
+
+
+@stereotype.evaluation_method("pose_diff")
+class PoseDiff:
+    def __init__(self, _):
+        pass
+    
+    def evaluate(self, output_list, data_list):
+        results = {namespace.TensorBoard.SCALAR: {}}
+        rot_angle_list = []
+        trans_dist_list = []
+        for output, data in zip(output_list, data_list):
+            gt_pose_9d = data['best_to_world_pose_9d']
+            pred_pose_9d = output['pred_pose_9d']
+            gt_rot_6d = gt_pose_9d[:, :6]
+            gt_trans = gt_pose_9d[:, 6:]
+            pred_rot_6d = pred_pose_9d[:, :6]
+            pred_trans = pred_pose_9d[:, 6:]
+            gt_rot_mat = PoseUtil.rotation_6d_to_matrix_tensor_batch(gt_rot_6d)
+            pred_rot_mat = PoseUtil.rotation_6d_to_matrix_tensor_batch(pred_rot_6d)
+            rotation_angles = PoseUtil.rotation_angle_distance(gt_rot_mat, pred_rot_mat)
+            
+            rot_angle_list.extend(list(rotation_angles))
+            trans_dist = torch.norm(gt_trans-pred_trans, dim=1).mean().item()
+            trans_dist_list.append(trans_dist)
+            
+
+        results[namespace.TensorBoard.SCALAR]["rot_diff"] = float(sum(rot_angle_list) / len(rot_angle_list))
+        results[namespace.TensorBoard.SCALAR]["trans_diff"] = float(sum(trans_dist_list) / len(trans_dist_list))
+        return results
+    
+
+
+@stereotype.evaluation_method("coverage_rate_increase")
+class ConverageRateIncrease:
+    def __init__(self, config):
+        self.config = config
+        self.renderer_path = config["renderer_path"]
+
+    def evaluate(self, output_list, data_list):
+        results = {namespace.TensorBoard.SCALAR: {}}
+        gt_coverate_increase_list = []
+        pred_coverate_increase_list = []
+        cr_diff_list = []
+        for output, data in zip(output_list, data_list):
+            scanned_cr = data['scanned_coverage_rate']
+            gt_cr = data["best_coverage_rate"]
+            scene_path_list = data['scene_path']
+            model_points_normals_list = data['model_points_normals']
+            scanned_view_pts_list = data['scanned_target_pts_list']
+            pred_pose_9ds = output['pred_pose_9d']
+            nO_to_nL_pose_batch = data["nO_to_nL_pose"]
+            voxel_threshold_list = data["voxel_threshold"]
+            filter_degree_list = data["filter_degree"]
+            first_frame_to_world = data["first_frame_to_world"]
+            pred_n_to_world_pose_mats = torch.eye(4, device=pred_pose_9ds.device).unsqueeze(0).repeat(pred_pose_9ds.shape[0], 1, 1)
+            pred_n_to_world_pose_mats[:,:3,:3] = PoseUtil.rotation_6d_to_matrix_tensor_batch(pred_pose_9ds[:, :6])
+            pred_n_to_world_pose_mats[:,:3,3] = pred_pose_9ds[:, 6:]
+            pred_n_to_world_pose_mats = torch.matmul(first_frame_to_world, pred_n_to_world_pose_mats)
+            for idx in range(len(scanned_cr)):
+                model_points_normals = model_points_normals_list[idx]
+                scanned_view_pts = scanned_view_pts_list[idx]
+                voxel_threshold = voxel_threshold_list[idx]
+                model_pts = model_points_normals[:,:3]
+                down_sampled_model_pts = PtsUtil.voxel_downsample_point_cloud(model_pts, voxel_threshold)
+                old_scanned_cr = self.compute_coverage_rate(scanned_view_pts, None, down_sampled_model_pts, threshold=voxel_threshold)
+                gt_coverate_increase_list.append(gt_cr[idx]-old_scanned_cr)
+
+                scene_path = scene_path_list[idx]
+                pred_pose = pred_n_to_world_pose_mats[idx]
+            
+                filter_degree = filter_degree_list[idx]
+                nO_to_nL_pose = nO_to_nL_pose_batch[idx]
+                try:
+                    new_pts, _ = RenderUtil.render_pts(pred_pose, scene_path, self.renderer_path, model_points_normals, voxel_threshold=voxel_threshold, filter_degree=filter_degree, nO_to_nL_pose=nO_to_nL_pose)
+                    pred_cr = self.compute_coverage_rate(scanned_view_pts, new_pts, down_sampled_model_pts, threshold=voxel_threshold)
+                except Exception as e:
+                    Log.warning(f"Error in scene {scene_path}, {e}")
+                    pred_cr = old_scanned_cr
+                pred_coverate_increase_list.append(pred_cr-old_scanned_cr)
+                cr_diff_list.append(gt_cr[idx]-pred_cr)
+                
+        results[namespace.TensorBoard.SCALAR]["gt_cr_increase"] = float(sum(gt_coverate_increase_list) / len(gt_coverate_increase_list))
+        results[namespace.TensorBoard.SCALAR]["pred_cr_increase"] = float(sum(pred_coverate_increase_list) / len(pred_coverate_increase_list))
+        results[namespace.TensorBoard.SCALAR]["cr_diff"] = float(sum(cr_diff_list) / len(cr_diff_list))
+        return results
+    
+    def compute_coverage_rate(self, scanned_view_pts, new_pts, model_pts, threshold=0.005):
+        if new_pts is not None:
+            new_scanned_view_pts = scanned_view_pts + [new_pts]
+        else:
+            new_scanned_view_pts = scanned_view_pts
+        combined_point_cloud = np.vstack(new_scanned_view_pts)
+        down_sampled_combined_point_cloud = PtsUtil.voxel_downsample_point_cloud(combined_point_cloud,threshold)
+        return ReconstructionUtil.compute_coverage_rate(model_pts, down_sampled_combined_point_cloud, threshold)
+
+    
+    
+    

core/global_pts_pipeline.py

@@ -0,0 +1,98 @@
+import torch
+from torch import nn
+import PytorchBoot.namespace as namespace
+import PytorchBoot.stereotype as stereotype
+from PytorchBoot.factory.component_factory import ComponentFactory
+from PytorchBoot.utils import Log
+
+
+@stereotype.pipeline("nbv_reconstruction_pipeline_global")
+class NBVReconstructionGlobalPointsPipeline(nn.Module):
+    def __init__(self, config):
+        super(NBVReconstructionGlobalPointsPipeline, self).__init__()
+        self.config = config
+        self.module_config = config["modules"]
+        self.pts_encoder = ComponentFactory.create(namespace.Stereotype.MODULE, self.module_config["pts_encoder"])
+        self.pose_encoder = ComponentFactory.create(namespace.Stereotype.MODULE, self.module_config["pose_encoder"])
+        self.seq_encoder = ComponentFactory.create(namespace.Stereotype.MODULE, self.module_config["seq_encoder"])
+        self.view_finder = ComponentFactory.create(namespace.Stereotype.MODULE, self.module_config["view_finder"])
+        self.eps = float(self.config["eps"])
+        self.enable_global_scanned_feat = self.config["global_scanned_feat"]
+        
+    def forward(self, data):
+        mode = data["mode"]
+        
+        if mode == namespace.Mode.TRAIN:
+            return self.forward_train(data)
+        elif mode == namespace.Mode.TEST:
+            return self.forward_test(data)
+        else:
+            Log.error("Unknown mode: {}".format(mode), True)
+    
+    def pertube_data(self, gt_delta_9d):
+        bs = gt_delta_9d.shape[0]
+        random_t = torch.rand(bs, device=gt_delta_9d.device) * (1. - self.eps) + self.eps
+        random_t = random_t.unsqueeze(-1)
+        mu, std = self.view_finder.marginal_prob(gt_delta_9d, random_t)
+        std = std.view(-1, 1)
+        z = torch.randn_like(gt_delta_9d)
+        perturbed_x = mu + z * std
+        target_score = - z * std / (std ** 2)
+        return perturbed_x, random_t, target_score, std
+    
+    def forward_train(self, data):
+        main_feat = self.get_main_feat(data)
+        ''' get std '''
+        best_to_world_pose_9d_batch = data["best_to_world_pose_9d"]
+        perturbed_x, random_t, target_score, std = self.pertube_data(best_to_world_pose_9d_batch)
+        input_data = {
+            "sampled_pose": perturbed_x,
+            "t": random_t,
+            "main_feat": main_feat,
+        }
+        estimated_score = self.view_finder(input_data)
+        output = {
+            "estimated_score": estimated_score,
+            "target_score": target_score,
+            "std": std
+        }
+        return output
+    
+    def forward_test(self,data):
+        main_feat = self.get_main_feat(data)
+        repeat_num = data.get("repeat_num", 50)
+        main_feat = main_feat.repeat(repeat_num, 1)
+        estimated_delta_rot_9d, in_process_sample = self.view_finder.next_best_view(
+            main_feat
+        )
+        result = {
+            "pred_pose_9d": estimated_delta_rot_9d,
+            "in_process_sample": in_process_sample,
+        }
+        return result
+    
+    
+    def get_main_feat(self, data):
+        scanned_n_to_world_pose_9d_batch = data['scanned_n_to_world_pose_9d']
+
+        device = next(self.parameters()).device
+
+        feat_seq_list = []
+        
+        for scanned_n_to_world_pose_9d in scanned_n_to_world_pose_9d_batch:
+            scanned_n_to_world_pose_9d = scanned_n_to_world_pose_9d.to(device)
+            feat_seq_list.append(self.pose_encoder.encode_pose(scanned_n_to_world_pose_9d))
+
+        main_feat = self.seq_encoder.encode_sequence(feat_seq_list)
+        
+        
+        combined_scanned_pts_batch = data['combined_scanned_pts']   
+        global_scanned_feat = self.pts_encoder.encode_points(combined_scanned_pts_batch)
+        main_feat = torch.cat([main_feat, global_scanned_feat], dim=-1)
+        
+        
+        if torch.isnan(main_feat).any():
+            Log.error("nan in main_feat", True)
+        
+        return main_feat
+    

core/local_pts_pipeline.py

@@ -0,0 +1,99 @@
+import torch
+from torch import nn
+import PytorchBoot.namespace as namespace
+import PytorchBoot.stereotype as stereotype
+from PytorchBoot.factory.component_factory import ComponentFactory
+from PytorchBoot.utils import Log
+
+@stereotype.pipeline("nbv_reconstruction_pipeline_local")
+class NBVReconstructionLocalPointsPipeline(nn.Module):
+    def __init__(self, config):
+        super(NBVReconstructionLocalPointsPipeline, self).__init__()
+        self.config = config
+        self.module_config = config["modules"]
+        self.pts_encoder = ComponentFactory.create(namespace.Stereotype.MODULE, self.module_config["pts_encoder"])
+        self.pose_encoder = ComponentFactory.create(namespace.Stereotype.MODULE, self.module_config["pose_encoder"])
+        self.seq_encoder = ComponentFactory.create(namespace.Stereotype.MODULE, self.module_config["seq_encoder"])
+        self.view_finder = ComponentFactory.create(namespace.Stereotype.MODULE, self.module_config["view_finder"])
+        self.eps = float(self.config["eps"])
+        self.enable_global_scanned_feat = self.config["global_scanned_feat"]
+        
+    def forward(self, data):
+        mode = data["mode"]
+        
+        if mode == namespace.Mode.TRAIN:
+            return self.forward_train(data)
+        elif mode == namespace.Mode.TEST:
+            return self.forward_test(data)
+        else:
+            Log.error("Unknown mode: {}".format(mode), True)
+    
+    def pertube_data(self, gt_delta_9d):
+        bs = gt_delta_9d.shape[0]
+        random_t = torch.rand(bs, device=gt_delta_9d.device) * (1. - self.eps) + self.eps
+        random_t = random_t.unsqueeze(-1)
+        mu, std = self.view_finder.marginal_prob(gt_delta_9d, random_t)
+        std = std.view(-1, 1)
+        z = torch.randn_like(gt_delta_9d)
+        perturbed_x = mu + z * std
+        target_score = - z * std / (std ** 2)
+        return perturbed_x, random_t, target_score, std
+    
+    def forward_train(self, data):
+        main_feat = self.get_main_feat(data)
+        ''' get std '''
+        best_to_world_pose_9d_batch = data["best_to_world_pose_9d"]
+        perturbed_x, random_t, target_score, std = self.pertube_data(best_to_world_pose_9d_batch)
+        input_data = {
+            "sampled_pose": perturbed_x,
+            "t": random_t,
+            "main_feat": main_feat,
+        }
+        estimated_score = self.view_finder(input_data)
+        output = {
+            "estimated_score": estimated_score,
+            "target_score": target_score,
+            "std": std
+        }
+        return output
+    
+    def forward_test(self,data):
+        main_feat = self.get_main_feat(data)
+        repeat_num = data.get("repeat_num", 50)
+        main_feat = main_feat.repeat(repeat_num, 1)
+        estimated_delta_rot_9d, in_process_sample = self.view_finder.next_best_view(
+            main_feat
+        )
+        result = {
+            "pred_pose_9d": estimated_delta_rot_9d,
+            "in_process_sample": in_process_sample,
+        }
+        return result
+    
+    
+    def get_main_feat(self, data):
+        scanned_pts_batch = data['scanned_pts']
+        scanned_n_to_world_pose_9d_batch = data['scanned_n_to_world_pose_9d']
+        device = next(self.parameters()).device
+        feat_seq_list = []
+    
+        for scanned_pts,scanned_n_to_world_pose_9d in zip(scanned_pts_batch,scanned_n_to_world_pose_9d_batch):
+            scanned_pts = scanned_pts.to(device)
+            scanned_n_to_world_pose_9d = scanned_n_to_world_pose_9d.to(device)
+            pts_feat = self.pts_encoder.encode_points(scanned_pts)
+            pose_feat = self.pose_encoder.encode_pose(scanned_n_to_world_pose_9d)
+            seq_feat = torch.cat([pts_feat, pose_feat], dim=-1)
+            feat_seq_list.append(seq_feat)
+        main_feat = self.seq_encoder.encode_sequence(feat_seq_list)
+        
+        if self.enable_global_scanned_feat:
+            combined_scanned_pts_batch = data['combined_scanned_pts']   
+            global_scanned_feat = self.pts_encoder.encode_points(combined_scanned_pts_batch)
+            main_feat = torch.cat([main_feat, global_scanned_feat], dim=-1)
+        
+        
+        if torch.isnan(main_feat).any():
+            Log.error("nan in main_feat", True)
+        
+        return main_feat
+    

core/loss.py

@@ -0,0 +1,27 @@
+import torch
+import PytorchBoot.stereotype as stereotype
+
+@stereotype.loss_function("gf_loss")
+class GFLoss:
+    def __init__(self, _):
+        pass
+
+    def compute(self, output, _):
+        estimated_score = output['estimated_score']
+        target_score = output['target_score']
+        std = output['std']
+        bs = estimated_score.shape[0]
+        loss_weighting = std ** 2
+        loss = torch.mean(torch.sum((loss_weighting * (estimated_score - target_score) ** 2).view(bs, -1), dim=-1))
+        return loss
+    
+@stereotype.loss_function("mse_loss")
+class MSELoss:
+    def __init__(self,_):
+        pass
+    
+    def compute(self, output, _):
+        pred_pose = output["pred"]
+        gt_pose = output["gt"]
+        loss = torch.mean(torch.sum((pred_pose - gt_pose) ** 2, dim=-1))
+        return loss

core/nbv_dataset.py

@@ -0,0 +1,282 @@
+import numpy as np
+from PytorchBoot.dataset import BaseDataset
+import PytorchBoot.namespace as namespace
+import PytorchBoot.stereotype as stereotype
+from PytorchBoot.config import ConfigManager
+from PytorchBoot.utils.log_util import Log
+import torch
+import os
+import sys
+import time
+
+sys.path.append(r"/data/hofee/project/nbv_rec/nbv_reconstruction")
+
+from utils.data_load import DataLoadUtil
+from utils.pose import PoseUtil
+from utils.pts import PtsUtil
+
+
+@stereotype.dataset("nbv_reconstruction_dataset")
+class NBVReconstructionDataset(BaseDataset):
+    def __init__(self, config):
+        super(NBVReconstructionDataset, self).__init__(config)
+        self.config = config
+        self.root_dir = config["root_dir"]
+        self.split_file_path = config["split_file"]
+        self.scene_name_list = self.load_scene_name_list()
+        self.datalist = self.get_datalist()
+
+        self.pts_num = config["pts_num"]
+        self.type = config["type"]
+        self.cache = config.get("cache")
+        self.load_from_preprocess = config.get("load_from_preprocess", False)
+
+        if self.type == namespace.Mode.TEST:
+            #self.model_dir = config["model_dir"]
+            self.filter_degree = config["filter_degree"]
+        if self.type == namespace.Mode.TRAIN:
+            scale_ratio = 1
+            self.datalist = self.datalist*scale_ratio
+        if self.cache:
+            expr_root = ConfigManager.get("runner", "experiment", "root_dir")
+            expr_name = ConfigManager.get("runner", "experiment", "name")
+            self.cache_dir = os.path.join(expr_root, expr_name, "cache")
+            # self.preprocess_cache()
+
+    def load_scene_name_list(self):
+        scene_name_list = []
+        with open(self.split_file_path, "r") as f:
+            for line in f:
+                scene_name = line.strip()
+                scene_name_list.append(scene_name)
+        return scene_name_list
+
+    def get_datalist(self):
+        datalist = []
+        for scene_name in self.scene_name_list:
+            seq_num = DataLoadUtil.get_label_num(self.root_dir, scene_name)
+            scene_max_coverage_rate = 0
+            max_coverage_rate_list = []
+
+            for seq_idx in range(seq_num):
+                label_path = DataLoadUtil.get_label_path(
+                    self.root_dir, scene_name, seq_idx
+                )
+                label_data = DataLoadUtil.load_label(label_path)
+                max_coverage_rate = label_data["max_coverage_rate"]
+                if max_coverage_rate > scene_max_coverage_rate:
+                    scene_max_coverage_rate = max_coverage_rate
+                max_coverage_rate_list.append(max_coverage_rate)
+                
+            if max_coverage_rate_list:
+                mean_coverage_rate = np.mean(max_coverage_rate_list)
+
+            for seq_idx in range(seq_num):
+                label_path = DataLoadUtil.get_label_path(
+                    self.root_dir, scene_name, seq_idx
+                )
+                label_data = DataLoadUtil.load_label(label_path)
+                if max_coverage_rate_list[seq_idx] > mean_coverage_rate - 0.1:
+                    for data_pair in label_data["data_pairs"]:
+                        scanned_views = data_pair[0]
+                        next_best_view = data_pair[1]
+                        datalist.append(
+                            {
+                                "scanned_views": scanned_views,
+                                "next_best_view": next_best_view,
+                                "seq_max_coverage_rate": max_coverage_rate,
+                                "scene_name": scene_name,
+                                "label_idx": seq_idx,
+                                "scene_max_coverage_rate": scene_max_coverage_rate,
+                            }
+                        )
+        return datalist
+
+    def preprocess_cache(self):
+        Log.info("preprocessing cache...")
+        for item_idx in range(len(self.datalist)):
+            self.__getitem__(item_idx)
+        Log.success("finish preprocessing cache.")
+
+    def load_from_cache(self, scene_name, curr_frame_idx):
+        cache_name = f"{scene_name}_{curr_frame_idx}.txt"
+        cache_path = os.path.join(self.cache_dir, cache_name)
+        if os.path.exists(cache_path):
+            data = np.loadtxt(cache_path)
+            return data
+        else:
+            return None
+
+    def save_to_cache(self, scene_name, curr_frame_idx, data):
+        cache_name = f"{scene_name}_{curr_frame_idx}.txt"
+        cache_path = os.path.join(self.cache_dir, cache_name)
+        try:
+            np.savetxt(cache_path, data)
+        except Exception as e:
+            Log.error(f"Save cache failed: {e}")
+
+    def voxel_downsample_with_mapping(self, point_cloud, voxel_size=0.003):
+        voxel_indices = np.floor(point_cloud / voxel_size).astype(np.int32)
+        unique_voxels, inverse, counts = np.unique(voxel_indices, axis=0, return_inverse=True, return_counts=True)
+        idx_sort = np.argsort(inverse)
+        idx_unique = idx_sort[np.cumsum(counts)-counts]
+        downsampled_points = point_cloud[idx_unique]
+        return downsampled_points, inverse
+
+
+    def __getitem__(self, index):
+        data_item_info = self.datalist[index]
+        scanned_views = data_item_info["scanned_views"]
+        nbv = data_item_info["next_best_view"]
+        max_coverage_rate = data_item_info["seq_max_coverage_rate"]
+        scene_name = data_item_info["scene_name"]
+        (
+            scanned_views_pts,
+            scanned_coverages_rate,
+            scanned_n_to_world_pose,
+        ) = ([], [], [])
+        #start_time = time.time()
+        start_indices = [0]
+        total_points = 0
+        for view in scanned_views:
+            frame_idx = view[0]
+            coverage_rate = view[1]
+            view_path = DataLoadUtil.get_path(self.root_dir, scene_name, frame_idx)
+            cam_info = DataLoadUtil.load_cam_info(view_path, binocular=True)
+            
+            n_to_world_pose = cam_info["cam_to_world"]  
+            target_point_cloud = (
+                DataLoadUtil.load_from_preprocessed_pts(view_path)
+            )
+            downsampled_target_point_cloud = PtsUtil.random_downsample_point_cloud(
+                target_point_cloud, self.pts_num
+            )
+            scanned_views_pts.append(downsampled_target_point_cloud)
+            scanned_coverages_rate.append(coverage_rate)
+            n_to_world_6d = PoseUtil.matrix_to_rotation_6d_numpy(
+                np.asarray(n_to_world_pose[:3, :3])
+            )
+            n_to_world_trans = n_to_world_pose[:3, 3]
+            n_to_world_9d = np.concatenate([n_to_world_6d, n_to_world_trans], axis=0)
+            scanned_n_to_world_pose.append(n_to_world_9d)
+            total_points += len(downsampled_target_point_cloud)
+            start_indices.append(total_points)
+
+
+        #end_time = time.time()
+        #Log.info(f"load data time: {end_time - start_time}")
+        nbv_idx, nbv_coverage_rate = nbv[0], nbv[1]
+        nbv_path = DataLoadUtil.get_path(self.root_dir, scene_name, nbv_idx)
+        cam_info = DataLoadUtil.load_cam_info(nbv_path)
+        best_frame_to_world = cam_info["cam_to_world"]
+
+        best_to_world_6d = PoseUtil.matrix_to_rotation_6d_numpy(
+            np.asarray(best_frame_to_world[:3, :3])
+        )
+        best_to_world_trans = best_frame_to_world[:3, 3]
+        best_to_world_9d = np.concatenate(
+            [best_to_world_6d, best_to_world_trans], axis=0
+        )
+        
+        combined_scanned_views_pts = np.concatenate(scanned_views_pts, axis=0)
+        voxel_downsampled_combined_scanned_pts_np, inverse = self.voxel_downsample_with_mapping(combined_scanned_views_pts, 0.003)
+        random_downsampled_combined_scanned_pts_np, random_downsample_idx = PtsUtil.random_downsample_point_cloud(voxel_downsampled_combined_scanned_pts_np, self.pts_num, require_idx=True)
+
+        # all_idx_unique = np.arange(len(voxel_downsampled_combined_scanned_pts_np))
+        # all_random_downsample_idx = all_idx_unique[random_downsample_idx]
+        # scanned_pts_mask = []
+        # for idx, start_idx in enumerate(start_indices):
+        #     if idx == len(start_indices) - 1:
+        #         break
+        #     end_idx = start_indices[idx+1]
+        #     view_inverse = inverse[start_idx:end_idx]
+        #     view_unique_downsampled_idx = np.unique(view_inverse)
+        #     view_unique_downsampled_idx_set = set(view_unique_downsampled_idx)
+        #     mask = np.array([idx in view_unique_downsampled_idx_set for idx in all_random_downsample_idx])
+        #     #scanned_pts_mask.append(mask)
+        data_item = {
+            "scanned_pts": np.asarray(scanned_views_pts, dtype=np.float32), # Ndarray(S x Nv x 3)
+            "combined_scanned_pts": np.asarray(random_downsampled_combined_scanned_pts_np, dtype=np.float32), # Ndarray(N x 3)
+            #"scanned_pts_mask": np.asarray(scanned_pts_mask, dtype=np.bool), # Ndarray(N)
+            "scanned_coverage_rate": scanned_coverages_rate, # List(S): Float, range(0, 1)
+            "scanned_n_to_world_pose_9d": np.asarray(scanned_n_to_world_pose, dtype=np.float32), # Ndarray(S x 9)
+            "best_coverage_rate": nbv_coverage_rate, # Float, range(0, 1)
+            "best_to_world_pose_9d": np.asarray(best_to_world_9d, dtype=np.float32), # Ndarray(9)
+            "seq_max_coverage_rate": max_coverage_rate, # Float, range(0, 1)
+            "scene_name": scene_name, # String
+        }
+
+        return data_item
+
+    def __len__(self):
+        return len(self.datalist)
+
+    def get_collate_fn(self):
+        def collate_fn(batch):
+            collate_data = {}
+            
+            ''' ------ Varialbe Length ------ '''
+            
+            collate_data["scanned_pts"] = [
+                torch.tensor(item["scanned_pts"]) for item in batch
+            ]
+            collate_data["scanned_n_to_world_pose_9d"] = [
+                torch.tensor(item["scanned_n_to_world_pose_9d"]) for item in batch
+            ]
+            # collate_data["scanned_pts_mask"] = [
+            #     torch.tensor(item["scanned_pts_mask"]) for item in batch
+            # ]
+            ''' ------ Fixed Length ------ '''
+            
+            collate_data["best_to_world_pose_9d"] = torch.stack(
+                [torch.tensor(item["best_to_world_pose_9d"]) for item in batch]
+            )
+            collate_data["combined_scanned_pts"] = torch.stack(
+                [torch.tensor(item["combined_scanned_pts"]) for item in batch]
+            )
+            
+            for key in batch[0].keys():
+                if key not in [
+                    "scanned_pts",
+                    "scanned_n_to_world_pose_9d",
+                    "best_to_world_pose_9d",
+                    "combined_scanned_pts",
+                    "scanned_pts_mask",
+                ]:
+                    collate_data[key] = [item[key] for item in batch]
+            return collate_data
+
+        return collate_fn
+
+
+# -------------- Debug ---------------- #
+if __name__ == "__main__":
+    import torch
+
+    seed = 0
+    torch.manual_seed(seed)
+    np.random.seed(seed)
+    config = {
+        "root_dir": "/data/hofee/nbv_rec_part2_preprocessed",
+        "source": "nbv_reconstruction_dataset",
+        "split_file": "/data/hofee/data/sample.txt",
+        "load_from_preprocess": True,
+        "ratio": 0.5,
+        "batch_size": 2,
+        "filter_degree": 75,
+        "num_workers": 0,
+        "pts_num": 4096,
+        "type": namespace.Mode.TRAIN,
+    }
+    ds = NBVReconstructionDataset(config)
+    print(len(ds))
+    # ds.__getitem__(10)
+    dl = ds.get_loader(shuffle=True)
+    for idx, data in enumerate(dl):
+        data = ds.process_batch(data, "cuda:0")
+        print(data)
+        # ------  Debug Start ------
+        import ipdb
+
+        ipdb.set_trace()
+        # ------  Debug End ------

core/old_seq_dataset.py

@@ -0,0 +1,154 @@
+import numpy as np
+from PytorchBoot.dataset import BaseDataset
+import PytorchBoot.namespace as namespace
+import PytorchBoot.stereotype as stereotype
+from PytorchBoot.utils.log_util import Log
+import torch
+import os
+import sys
+sys.path.append(r"/home/data/hofee/project/nbv_rec/nbv_reconstruction")
+
+from utils.data_load import DataLoadUtil
+from utils.pose import PoseUtil
+from utils.pts import PtsUtil
+
+@stereotype.dataset("old_seq_nbv_reconstruction_dataset")
+class SeqNBVReconstructionDataset(BaseDataset):
+    def __init__(self, config):
+        super(SeqNBVReconstructionDataset, self).__init__(config)
+        self.type = config["type"]
+        if self.type != namespace.Mode.TEST:
+            Log.error("Dataset <seq_nbv_reconstruction_dataset> Only support test mode", terminate=True)
+        self.config = config
+        self.root_dir = config["root_dir"]
+        self.split_file_path = config["split_file"]
+        self.scene_name_list = self.load_scene_name_list()
+        self.datalist = self.get_datalist()
+        self.pts_num = config["pts_num"]
+        
+        self.model_dir = config["model_dir"]
+        self.filter_degree = config["filter_degree"]
+        self.load_from_preprocess = config.get("load_from_preprocess", False)
+        
+
+    def load_scene_name_list(self):
+        scene_name_list = []
+        with open(self.split_file_path, "r") as f:
+            for line in f:
+                scene_name = line.strip()
+                scene_name_list.append(scene_name)
+        return scene_name_list
+    
+    def get_datalist(self):
+        datalist = []
+        for scene_name in self.scene_name_list:
+            seq_num = DataLoadUtil.get_label_num(self.root_dir, scene_name)
+            scene_max_coverage_rate = 0
+            scene_max_cr_idx = 0
+
+            for seq_idx in range(seq_num):
+                label_path = DataLoadUtil.get_label_path(self.root_dir, scene_name, seq_idx)
+                label_data = DataLoadUtil.load_label(label_path)
+                max_coverage_rate = label_data["max_coverage_rate"]
+                if max_coverage_rate > scene_max_coverage_rate:
+                    scene_max_coverage_rate = max_coverage_rate
+                    scene_max_cr_idx = seq_idx
+        
+            label_path = DataLoadUtil.get_label_path(self.root_dir, scene_name, scene_max_cr_idx)
+            label_data = DataLoadUtil.load_label(label_path)
+            first_frame = label_data["best_sequence"][0]
+            best_seq_len = len(label_data["best_sequence"])
+            datalist.append({
+                    "scene_name": scene_name,
+                    "first_frame": first_frame,
+                    "max_coverage_rate": scene_max_coverage_rate,
+                    "best_seq_len": best_seq_len,
+                    "label_idx": scene_max_cr_idx,
+                })
+        return datalist
+
+    def __getitem__(self, index):
+        data_item_info = self.datalist[index]
+        first_frame_idx = data_item_info["first_frame"][0]
+        first_frame_coverage = data_item_info["first_frame"][1]
+        max_coverage_rate = data_item_info["max_coverage_rate"]
+        scene_name = data_item_info["scene_name"]
+        first_cam_info = DataLoadUtil.load_cam_info(DataLoadUtil.get_path(self.root_dir, scene_name, first_frame_idx), binocular=True)
+        first_view_path = DataLoadUtil.get_path(self.root_dir, scene_name, first_frame_idx)
+        first_left_cam_pose = first_cam_info["cam_to_world"]
+        first_center_cam_pose = first_cam_info["cam_to_world_O"]
+        first_target_point_cloud = DataLoadUtil.load_from_preprocessed_pts(first_view_path)
+        first_pts_num = first_target_point_cloud.shape[0]
+        first_downsampled_target_point_cloud = PtsUtil.random_downsample_point_cloud(first_target_point_cloud, self.pts_num)
+        first_to_world_rot_6d = PoseUtil.matrix_to_rotation_6d_numpy(np.asarray(first_left_cam_pose[:3,:3]))
+        first_to_world_trans = first_left_cam_pose[:3,3]
+        first_to_world_9d = np.concatenate([first_to_world_rot_6d, first_to_world_trans], axis=0)
+        diag = DataLoadUtil.get_bbox_diag(self.model_dir, scene_name)
+        voxel_threshold = diag*0.02
+        first_O_to_first_L_pose = np.dot(np.linalg.inv(first_left_cam_pose), first_center_cam_pose)
+        scene_path = os.path.join(self.root_dir, scene_name)
+        model_points_normals = DataLoadUtil.load_points_normals(self.root_dir, scene_name)
+
+        data_item = {
+            "first_pts_num": np.asarray(
+                first_pts_num, dtype=np.int32
+            ),
+            "first_pts": np.asarray([first_downsampled_target_point_cloud],dtype=np.float32),
+            "combined_scanned_pts": np.asarray(first_downsampled_target_point_cloud,dtype=np.float32),
+            "first_to_world_9d": np.asarray([first_to_world_9d],dtype=np.float32),
+            "scene_name": scene_name,
+            "max_coverage_rate": max_coverage_rate,
+            "voxel_threshold": voxel_threshold,
+            "filter_degree": self.filter_degree,
+            "O_to_L_pose": first_O_to_first_L_pose,
+            "first_frame_coverage": first_frame_coverage,
+            "scene_path": scene_path,
+            "model_points_normals": model_points_normals,
+            "best_seq_len": data_item_info["best_seq_len"],
+            "first_frame_id": first_frame_idx,
+        }
+        return data_item
+
+    def __len__(self):
+        return len(self.datalist)
+    
+    def get_collate_fn(self):
+        def collate_fn(batch):
+            collate_data = {}
+            collate_data["first_pts"] = [torch.tensor(item['first_pts']) for item in batch]
+            collate_data["first_to_world_9d"] = [torch.tensor(item['first_to_world_9d']) for item in batch]
+            collate_data["combined_scanned_pts"] = torch.stack([torch.tensor(item['combined_scanned_pts']) for item in batch])
+            for key in batch[0].keys():
+                if key not in ["first_pts", "first_to_world_9d", "combined_scanned_pts"]:
+                    collate_data[key] = [item[key] for item in batch]
+            return collate_data
+        return collate_fn
+
+# -------------- Debug ---------------- #
+if __name__ == "__main__":
+    import torch
+    seed = 0
+    torch.manual_seed(seed)
+    np.random.seed(seed)
+    config = {
+        "root_dir": "/home/data/hofee/project/nbv_rec/data/nbv_rec_data_512_preproc_npy",
+        "split_file": "/home/data/hofee/project/nbv_rec/data/OmniObject3d_train.txt",
+        "model_dir": "/home/data/hofee/project/nbv_rec/data/scaled_object_meshes",
+        "ratio": 0.005,
+        "batch_size": 2,
+        "filter_degree": 75,
+        "num_workers": 0,
+        "pts_num": 32684,
+        "type": namespace.Mode.TEST,
+        "load_from_preprocess": True
+    }
+    ds = SeqNBVReconstructionDataset(config)
+    print(len(ds))
+    #ds.__getitem__(10)
+    dl = ds.get_loader(shuffle=True)
+    for idx, data in enumerate(dl):
+        data = ds.process_batch(data, "cuda:0")
+        print(data)
+        # ------  Debug Start ------
+        import ipdb;ipdb.set_trace()
+        # ------  Debug End ------+

core/pipeline.py

@@ -0,0 +1,140 @@
+import torch
+import time
+from torch import nn
+import PytorchBoot.namespace as namespace
+import PytorchBoot.stereotype as stereotype
+from PytorchBoot.factory.component_factory import ComponentFactory
+from PytorchBoot.utils import Log
+
+
+@stereotype.pipeline("nbv_reconstruction_pipeline")
+class NBVReconstructionPipeline(nn.Module):
+    def __init__(self, config):
+        super(NBVReconstructionPipeline, self).__init__()
+        self.config = config
+        self.module_config = config["modules"]
+        
+        self.pts_encoder = ComponentFactory.create(
+            namespace.Stereotype.MODULE, self.module_config["pts_encoder"]
+        )
+        self.pose_encoder = ComponentFactory.create(
+            namespace.Stereotype.MODULE, self.module_config["pose_encoder"]
+        )
+        self.seq_encoder = ComponentFactory.create(
+            namespace.Stereotype.MODULE, self.module_config["seq_encoder"]
+        )
+        self.view_finder = ComponentFactory.create(
+            namespace.Stereotype.MODULE, self.module_config["view_finder"]
+        )
+        
+
+        self.eps = float(self.config["eps"])
+
+    def forward(self, data):
+        mode = data["mode"]
+
+        if mode == namespace.Mode.TRAIN:
+            return self.forward_train(data)
+        elif mode == namespace.Mode.TEST:
+            return self.forward_test(data)
+        else:
+            Log.error("Unknown mode: {}".format(mode), True)
+
+    def pertube_data(self, gt_delta_9d):
+        bs = gt_delta_9d.shape[0]
+        random_t = (
+            torch.rand(bs, device=gt_delta_9d.device) * (1.0 - self.eps) + self.eps
+        )
+        random_t = random_t.unsqueeze(-1)
+        mu, std = self.view_finder.marginal_prob(gt_delta_9d, random_t)
+        std = std.view(-1, 1)
+        z = torch.randn_like(gt_delta_9d)
+        perturbed_x = mu + z * std
+        target_score = -z * std / (std**2)
+        return perturbed_x, random_t, target_score, std
+
+    def forward_train(self, data):
+        main_feat = self.get_main_feat(data)
+        """ get std """
+        best_to_world_pose_9d_batch = data["best_to_world_pose_9d"]
+        perturbed_x, random_t, target_score, std = self.pertube_data(
+            best_to_world_pose_9d_batch
+        )
+        input_data = {
+            "sampled_pose": perturbed_x,
+            "t": random_t,
+            "main_feat": main_feat,
+        }
+        estimated_score = self.view_finder(input_data)
+        output = {
+            "estimated_score": estimated_score,
+            "target_score": target_score,
+            "std": std,
+        }
+        return output
+
+    def forward_test(self, data):
+        main_feat = self.get_main_feat(data)
+        repeat_num = data.get("repeat_num", 1)
+        main_feat = main_feat.repeat(repeat_num, 1)
+        estimated_delta_rot_9d, in_process_sample = self.view_finder.next_best_view(
+            main_feat
+        )
+        result = {
+            "pred_pose_9d": estimated_delta_rot_9d,
+            "in_process_sample": in_process_sample,
+        }
+        return result
+
+    def get_main_feat(self, data):
+        scanned_n_to_world_pose_9d_batch = data[
+            "scanned_n_to_world_pose_9d"
+        ]  # List(B): Tensor(S x 9)
+        scanned_pts_mask_batch = data["scanned_pts_mask"] # List(B): Tensor(S x N)
+
+        scanned_pts_mask_batch = data["scanned_pts_mask"] # List(B): Tensor(N)
+
+        device = next(self.parameters()).device
+
+        embedding_list_batch = []
+
+        combined_scanned_pts_batch = data["combined_scanned_pts"]  # Tensor(B x N x 3)
+        global_scanned_feat, per_point_feat_batch = self.pts_encoder.encode_points(
+            combined_scanned_pts_batch, require_per_point_feat=True
+        )  # global_scanned_feat: Tensor(B x Dg)
+        batch_size = len(scanned_n_to_world_pose_9d_batch)
+        for i in range(batch_size):
+            seq_len = len(scanned_n_to_world_pose_9d_batch[i])
+            scanned_n_to_world_pose_9d = scanned_n_to_world_pose_9d_batch[i].to(device)  # Tensor(S x 9)
+            scanned_pts_mask = scanned_pts_mask_batch[i] # Tensor(S x N)
+            per_point_feat = per_point_feat_batch[i] # Tensor(N x Dp)
+            partial_point_feat_seq = []
+            for j in range(seq_len):
+                partial_per_point_feat = per_point_feat[scanned_pts_mask[j]]
+                if partial_per_point_feat.shape[0] == 0:
+                    partial_point_feat = torch.zeros(per_point_feat.shape[1], device=device)
+                else:
+                    partial_point_feat = torch.mean(partial_per_point_feat, dim=0) # Tensor(Dp)
+                partial_point_feat_seq.append(partial_point_feat)
+            partial_point_feat_seq = torch.stack(partial_point_feat_seq, dim=0) # Tensor(S x Dp)
+            
+            pose_feat_seq = self.pose_encoder.encode_pose(scanned_n_to_world_pose_9d)  # Tensor(S x Dp) 
+
+            seq_embedding = torch.cat([partial_point_feat_seq, pose_feat_seq], dim=-1)
+            
+            embedding_list_batch.append(seq_embedding) # List(B): Tensor(S x (Dp))
+        
+        seq_feat = self.seq_encoder.encode_sequence(embedding_list_batch) # Tensor(B x Ds)
+        main_feat = torch.cat([seq_feat, global_scanned_feat], dim=-1) # Tensor(B x (Ds+Dg))
+
+        if torch.isnan(main_feat).any():
+            for i in range(len(main_feat)):
+                if torch.isnan(main_feat[i]).any():
+                    scanned_pts_mask = scanned_pts_mask_batch[i]
+                    Log.info(f"scanned_pts_mask shape: {scanned_pts_mask.shape}")
+                    Log.info(f"scanned_pts_mask sum: {scanned_pts_mask.sum()}")
+                    import ipdb
+                    ipdb.set_trace()
+            Log.error("nan in main_feat", True)
+
+        return main_feat

core/seq_dataset.py

@@ -0,0 +1,209 @@
+import numpy as np
+from PytorchBoot.dataset import BaseDataset
+import PytorchBoot.namespace as namespace
+import PytorchBoot.stereotype as stereotype
+from PytorchBoot.config import ConfigManager
+from PytorchBoot.utils.log_util import Log
+import torch
+import os
+import sys
+
+sys.path.append(r"/media/hofee/data/project/python/nbv_reconstruction/nbv_reconstruction")
+
+from utils.data_load import DataLoadUtil
+from utils.pose import PoseUtil
+from utils.pts import PtsUtil
+
+
+@stereotype.dataset("seq_reconstruction_dataset")
+class SeqReconstructionDataset(BaseDataset):
+    def __init__(self, config):
+        super(SeqReconstructionDataset, self).__init__(config)
+        self.config = config
+        self.root_dir = config["root_dir"]
+        self.split_file_path = config["split_file"]
+        self.scene_name_list = self.load_scene_name_list()
+        self.datalist = self.get_datalist()
+
+        self.pts_num = config["pts_num"]
+        self.type = config["type"]
+        self.cache = config.get("cache")
+        self.load_from_preprocess = config.get("load_from_preprocess", False)
+
+        if self.type == namespace.Mode.TEST:
+            #self.model_dir = config["model_dir"]
+            self.filter_degree = config["filter_degree"]
+        if self.type == namespace.Mode.TRAIN:
+            scale_ratio = 1
+            self.datalist = self.datalist*scale_ratio
+        if self.cache:
+            expr_root = ConfigManager.get("runner", "experiment", "root_dir")
+            expr_name = ConfigManager.get("runner", "experiment", "name")
+            self.cache_dir = os.path.join(expr_root, expr_name, "cache")
+            # self.preprocess_cache()
+
+    def load_scene_name_list(self):
+        scene_name_list = []
+        with open(self.split_file_path, "r") as f:
+            for line in f:
+                scene_name = line.strip()
+                if not os.path.exists(os.path.join(self.root_dir, scene_name)):
+                    continue
+                scene_name_list.append(scene_name)
+        return scene_name_list    
+    
+    def get_scene_name_list(self):
+        return self.scene_name_list
+
+
+    def get_datalist(self):
+        datalist = []
+        total = len(self.scene_name_list)
+        for idx, scene_name in enumerate(self.scene_name_list):
+            print(f"processing {scene_name} ({idx}/{total})")
+            scene_max_cr_idx = 0
+            frame_len = DataLoadUtil.get_scene_seq_length(self.root_dir, scene_name)
+
+            for i in range(10,frame_len):
+                path = DataLoadUtil.get_path(self.root_dir, scene_name, i)
+                pts = DataLoadUtil.load_from_preprocessed_pts(path, "npy")
+                print(pts.shape)
+                if pts.shape[0] == 0:
+                    continue
+                else:
+                    break
+            print(i)
+            datalist.append({
+                "scene_name": scene_name,
+                "first_frame": i,
+                "best_seq_len": -1,
+                "max_coverage_rate": 1.0,
+                "label_idx": scene_max_cr_idx,
+            })  
+        return datalist
+
+    def preprocess_cache(self):
+        Log.info("preprocessing cache...")
+        for item_idx in range(len(self.datalist)):
+            self.__getitem__(item_idx)
+        Log.success("finish preprocessing cache.")
+
+    def load_from_cache(self, scene_name, curr_frame_idx):
+        cache_name = f"{scene_name}_{curr_frame_idx}.txt"
+        cache_path = os.path.join(self.cache_dir, cache_name)
+        if os.path.exists(cache_path):
+            data = np.loadtxt(cache_path)
+            return data
+        else:
+            return None
+
+    def save_to_cache(self, scene_name, curr_frame_idx, data):
+        cache_name = f"{scene_name}_{curr_frame_idx}.txt"
+        cache_path = os.path.join(self.cache_dir, cache_name)
+        try:
+            np.savetxt(cache_path, data)
+        except Exception as e:
+            Log.error(f"Save cache failed: {e}")
+
+    def seq_combined_pts(self, scene, frame_idx_list):
+        all_combined_pts = []   
+        for i in frame_idx_list:
+            path = DataLoadUtil.get_path(self.root_dir, scene, i)
+            pts = DataLoadUtil.load_from_preprocessed_pts(path,"npy")
+            if pts.shape[0] == 0:
+                continue
+            all_combined_pts.append(pts)
+        all_combined_pts = np.vstack(all_combined_pts)
+        downsampled_all_pts = PtsUtil.voxel_downsample_point_cloud(all_combined_pts, 0.003)
+        return downsampled_all_pts
+
+    def __getitem__(self, index):
+        data_item_info = self.datalist[index]
+        max_coverage_rate = data_item_info["max_coverage_rate"]
+        best_seq_len = data_item_info["best_seq_len"]
+        scene_name = data_item_info["scene_name"]
+        (
+            scanned_views_pts,
+            scanned_coverages_rate,
+            scanned_n_to_world_pose,
+        ) = ([], [], [])
+        view = data_item_info["first_frame"]
+        frame_idx = view
+        view_path = DataLoadUtil.get_path(self.root_dir, scene_name, frame_idx)
+        cam_info = DataLoadUtil.load_cam_info(view_path, binocular=True)
+        
+        n_to_world_pose = cam_info["cam_to_world"]  
+        target_point_cloud = (
+            DataLoadUtil.load_from_preprocessed_pts(view_path)
+        )
+        downsampled_target_point_cloud = PtsUtil.random_downsample_point_cloud(
+            target_point_cloud, self.pts_num
+        )
+        scanned_views_pts.append(downsampled_target_point_cloud)
+        
+        n_to_world_6d = PoseUtil.matrix_to_rotation_6d_numpy(
+            np.asarray(n_to_world_pose[:3, :3])
+        ) 
+        first_left_cam_pose = cam_info["cam_to_world"]
+        first_center_cam_pose = cam_info["cam_to_world_O"]
+        first_O_to_first_L_pose = np.dot(np.linalg.inv(first_left_cam_pose), first_center_cam_pose)
+        n_to_world_trans = n_to_world_pose[:3, 3]
+        n_to_world_9d = np.concatenate([n_to_world_6d, n_to_world_trans], axis=0)
+        scanned_n_to_world_pose.append(n_to_world_9d)
+
+        frame_list = []
+        for i in range(DataLoadUtil.get_scene_seq_length(self.root_dir, scene_name)):
+            frame_list.append(i)
+        gt_pts = self.seq_combined_pts(scene_name, frame_list)
+        data_item = {
+            "first_scanned_pts": np.asarray(scanned_views_pts, dtype=np.float32), # Ndarray(S x Nv x 3)
+            "first_scanned_n_to_world_pose_9d": np.asarray(scanned_n_to_world_pose, dtype=np.float32), # Ndarray(S x 9)
+            "seq_max_coverage_rate": max_coverage_rate, # Float, range(0, 1)
+            "best_seq_len": best_seq_len, # Int
+            "scene_name": scene_name, # String
+            "gt_pts": gt_pts, # Ndarray(N x 3)
+            "scene_path": os.path.join(self.root_dir, scene_name), # String
+            "O_to_L_pose": first_O_to_first_L_pose,
+        }
+
+        return data_item
+
+    def __len__(self):
+        return len(self.datalist)
+
+
+# -------------- Debug ---------------- #
+if __name__ == "__main__":
+    import torch
+    from tqdm import tqdm
+    import pickle
+    import os
+
+    seed = 0
+    torch.manual_seed(seed)
+    np.random.seed(seed)
+    
+    config = {
+        "root_dir": "/media/hofee/data/data/test_bottle/view",
+        "source": "seq_reconstruction_dataset",
+        "split_file": "/media/hofee/data/data/test_bottle/test_bottle.txt",
+        "load_from_preprocess": True,
+        "filter_degree": 75,
+        "num_workers": 0,
+        "pts_num": 8192,
+        "type": namespace.Mode.TEST,
+    }
+    
+    output_dir = "/media/hofee/data/data/test_bottle/preprocessed_dataset"
+    os.makedirs(output_dir, exist_ok=True)
+
+    ds = SeqReconstructionDataset(config)
+    for i in tqdm(range(len(ds)), desc="processing dataset"):
+        output_path = os.path.join(output_dir, f"item_{i}.pkl")
+        item = ds.__getitem__(i)
+        for key, value in item.items():
+            if isinstance(value, np.ndarray):
+                item[key] = value.tolist()
+        #import ipdb; ipdb.set_trace()
+        with open(output_path, "wb") as f:
+            pickle.dump(item, f)

core/seq_dataset_preprocessed.py

@@ -0,0 +1,82 @@
+import numpy as np
+from PytorchBoot.dataset import BaseDataset
+import PytorchBoot.namespace as namespace
+import PytorchBoot.stereotype as stereotype
+from PytorchBoot.config import ConfigManager
+from PytorchBoot.utils.log_util import Log
+import pickle
+import torch
+import os
+import sys
+
+sys.path.append(r"C:\Document\Local Project\nbv_rec\nbv_reconstruction")
+
+from utils.data_load import DataLoadUtil
+from utils.pose import PoseUtil
+from utils.pts import PtsUtil
+
+@stereotype.dataset("seq_reconstruction_dataset_preprocessed")
+class SeqReconstructionDatasetPreprocessed(BaseDataset):
+    def __init__(self, config):
+        super(SeqReconstructionDatasetPreprocessed, self).__init__(config)
+        self.config = config
+        self.root_dir = config["root_dir"]
+        self.real_root_dir = r"/media/hofee/repository/final_test_set/view"
+        self.item_list = os.listdir(self.root_dir)
+
+    def __getitem__(self, index):
+        data = pickle.load(open(os.path.join(self.root_dir, self.item_list[index]), "rb"))
+        data_item = {
+            "first_scanned_pts": np.asarray(data["first_scanned_pts"], dtype=np.float32), # Ndarray(S x Nv x 3)
+            "first_scanned_n_to_world_pose_9d": np.asarray(data["first_scanned_n_to_world_pose_9d"], dtype=np.float32), # Ndarray(S x 9)
+            "seq_max_coverage_rate": data["seq_max_coverage_rate"], # Float, range(0, 1)
+            "best_seq_len": data["best_seq_len"], # Int
+            "scene_name": data["scene_name"], # String
+            "gt_pts": np.asarray(data["gt_pts"], dtype=np.float32), # Ndarray(N x 3)
+            "scene_path": os.path.join(self.real_root_dir, data["scene_name"]), # String
+            "O_to_L_pose": np.asarray(data["O_to_L_pose"], dtype=np.float32),
+        }
+        return data_item
+
+    def __len__(self):
+        return len(self.item_list)
+
+# -------------- Debug ---------------- #
+if __name__ == "__main__":
+    import torch
+
+    seed = 0
+    torch.manual_seed(seed)
+    np.random.seed(seed)
+    '''
+    OmniObject3d_test:
+    root_dir: "H:\\AI\\Datasets\\packed_test_data"
+    model_dir: "H:\\AI\\Datasets\\scaled_object_meshes"
+    source: seq_reconstruction_dataset
+    split_file: "H:\\AI\\Datasets\\data_list\\OmniObject3d_test.txt"
+    type: test
+    filter_degree: 75
+    eval_list:
+      - pose_diff
+      - coverage_rate_increase
+    ratio: 0.1
+    batch_size: 1
+    num_workers: 12
+    pts_num: 8192
+    load_from_preprocess: True
+    '''
+    config = {
+        "root_dir": "/media/hofee/data/data/test_bottle/preprocessed_dataset",
+        "source": "seq_reconstruction_dataset",
+        "split_file": "H:\\AI\\Datasets\\data_list\\OmniObject3d_test.txt",
+        "load_from_preprocess": True,
+        "ratio": 1,
+        "filter_degree": 75,
+        "num_workers": 0,
+        "pts_num": 8192,
+        "type": "test",
+    }
+    ds = SeqReconstructionDataset(config)
+    print(len(ds))
+    print(ds.__getitem__(10))
+    

modules/func_lib/__init__.py

@@ -0,0 +1,6 @@
+from modules.func_lib.samplers import (
+    cond_ode_sampler
+)
+from modules.func_lib.sde import (
+    init_sde
+)

modules/func_lib/samplers.py

@@ -0,0 +1,95 @@
+import torch
+import numpy as np
+
+from scipy import integrate
+from utils.pose import PoseUtil
+
+
+def global_prior_likelihood(z, sigma_max):
+    """The likelihood of a Gaussian distribution with mean zero and
+    standard deviation sigma."""
+    # z: [bs, pose_dim]
+    shape = z.shape
+    N = np.prod(shape[1:])  # pose_dim
+    return -N / 2.0 * torch.log(2 * np.pi * sigma_max**2) - torch.sum(
+        z**2, dim=-1
+    ) / (2 * sigma_max**2)
+
+
+def cond_ode_sampler(
+    score_model,
+    data,
+    prior,
+    sde_coeff,
+    atol=1e-5,
+    rtol=1e-5,
+    device="cuda",
+    eps=1e-5,
+    T=1.0,
+    num_steps=None,
+    pose_mode="quat_wxyz",
+    denoise=True,
+    init_x=None,
+):
+    pose_dim = PoseUtil.get_pose_dim(pose_mode)
+    batch_size = data["main_feat"].shape[0]
+    init_x = (
+        prior((batch_size, pose_dim), T=T).to(device)
+        if init_x is None
+        else init_x + prior((batch_size, pose_dim), T=T).to(device)
+    )
+    shape = init_x.shape
+
+    def score_eval_wrapper(data):
+        """A wrapper of the score-based model for use by the ODE solver."""
+        with torch.no_grad():
+            score = score_model(data)
+        return score.cpu().numpy().reshape((-1,))
+
+    def ode_func(t, x):
+        """The ODE function for use by the ODE solver."""
+        x = torch.tensor(x.reshape(-1, pose_dim), dtype=torch.float32, device=device)
+        time_steps = torch.ones(batch_size, device=device).unsqueeze(-1) * t
+        drift, diffusion = sde_coeff(torch.tensor(t))
+        drift = drift.cpu().numpy()
+        diffusion = diffusion.cpu().numpy()
+        data["sampled_pose"] = x
+        data["t"] = time_steps
+        return drift - 0.5 * (diffusion**2) * score_eval_wrapper(data)
+
+    # Run the black-box ODE solver, note the
+    t_eval = None
+    if num_steps is not None:
+        # num_steps, from T -> eps
+        t_eval = np.linspace(T, eps, num_steps)
+    res = integrate.solve_ivp(
+        ode_func,
+        (T, eps),
+        init_x.reshape(-1).cpu().numpy(),
+        rtol=rtol,
+        atol=atol,
+        method="RK45",
+        t_eval=t_eval,
+    )
+    xs = torch.tensor(res.y, device=device).T.view(
+        -1, batch_size, pose_dim
+    )  # [num_steps, bs, pose_dim]
+    x = torch.tensor(res.y[:, -1], device=device).reshape(shape)  # [bs, pose_dim]
+    # denoise, using the predictor step in P-C sampler
+    if denoise:
+        # Reverse diffusion predictor for denoising
+        vec_eps = torch.ones((x.shape[0], 1), device=x.device) * eps
+        drift, diffusion = sde_coeff(vec_eps)
+        data["sampled_pose"] = x.float()
+        data["t"] = vec_eps
+        grad = score_model(data)
+        drift = drift - diffusion**2 * grad  # R-SDE
+        mean_x = x + drift * ((1 - eps) / (1000 if num_steps is None else num_steps))
+        x = mean_x
+
+    num_steps = xs.shape[0]
+    xs = xs.reshape(batch_size*num_steps, -1)
+    xs[:, :-3] = PoseUtil.normalize_rotation(xs[:, :-3], pose_mode)
+    xs = xs.reshape(num_steps, batch_size, -1)
+    x[:, :-3] = PoseUtil.normalize_rotation(x[:, :-3], pose_mode)
+    return xs.permute(1, 0, 2), x

modules/func_lib/sde.py

@@ -0,0 +1,121 @@
+import functools
+import torch
+import numpy as np
+
+
+# ----- VE SDE -----
+# ------------------
+def ve_marginal_prob(x, t, sigma_min=0.01, sigma_max=90):
+    std = sigma_min * (sigma_max / sigma_min) ** t
+    mean = x
+    return mean, std
+
+
+def ve_sde(t, sigma_min=0.01, sigma_max=90):
+    sigma = sigma_min * (sigma_max / sigma_min) ** t
+    drift_coeff = torch.tensor(0)
+    diffusion_coeff = sigma * torch.sqrt(torch.tensor(2 * (np.log(sigma_max) - np.log(sigma_min)), device=t.device))
+    return drift_coeff, diffusion_coeff
+
+
+def ve_prior(shape, sigma_min=0.01, sigma_max=90, T=1.0):
+    _, sigma_max_prior = ve_marginal_prob(None, T, sigma_min=sigma_min, sigma_max=sigma_max)
+    return torch.randn(*shape) * sigma_max_prior
+
+
+# ----- VP SDE -----
+# ------------------
+def vp_marginal_prob(x, t, beta_0=0.1, beta_1=20):
+    log_mean_coeff = -0.25 * t ** 2 * (beta_1 - beta_0) - 0.5 * t * beta_0
+    mean = torch.exp(log_mean_coeff) * x
+    std = torch.sqrt(1. - torch.exp(2. * log_mean_coeff))
+    return mean, std
+
+
+def vp_sde(t, beta_0=0.1, beta_1=20):
+    beta_t = beta_0 + t * (beta_1 - beta_0)
+    drift_coeff = -0.5 * beta_t
+    diffusion_coeff = torch.sqrt(beta_t)
+    return drift_coeff, diffusion_coeff
+
+
+def vp_prior(shape, beta_0=0.1, beta_1=20):
+    return torch.randn(*shape)
+
+
+# ----- sub-VP SDE -----
+# ----------------------
+def subvp_marginal_prob(x, t, beta_0, beta_1):
+    log_mean_coeff = -0.25 * t ** 2 * (beta_1 - beta_0) - 0.5 * t * beta_0
+    mean = torch.exp(log_mean_coeff) * x
+    std = 1 - torch.exp(2. * log_mean_coeff)
+    return mean, std
+
+
+def subvp_sde(t, beta_0, beta_1):
+    beta_t = beta_0 + t * (beta_1 - beta_0)
+    drift_coeff = -0.5 * beta_t
+    discount = 1. - torch.exp(-2 * beta_0 * t - (beta_1 - beta_0) * t ** 2)
+    diffusion_coeff = torch.sqrt(beta_t * discount)
+    return drift_coeff, diffusion_coeff
+
+
+def subvp_prior(shape, beta_0=0.1, beta_1=20):
+    return torch.randn(*shape)
+
+
+# ----- EDM SDE -----
+# ------------------
+def edm_marginal_prob(x, t, sigma_min=0.002, sigma_max=80):
+    std = t
+    mean = x
+    return mean, std
+
+
+def edm_sde(t, sigma_min=0.002, sigma_max=80):
+    drift_coeff = torch.tensor(0)
+    diffusion_coeff = torch.sqrt(2 * t)
+    return drift_coeff, diffusion_coeff
+
+
+def edm_prior(shape, sigma_min=0.002, sigma_max=80):
+    return torch.randn(*shape) * sigma_max
+
+
+def init_sde(sde_mode):
+    # the SDE-related hyperparameters are copied from https://github.com/yang-song/score_sde_pytorch
+    if sde_mode == 'edm':
+        sigma_min = 0.002
+        sigma_max = 80
+        eps = 0.002
+        prior_fn = functools.partial(edm_prior, sigma_min=sigma_min, sigma_max=sigma_max)
+        marginal_prob_fn = functools.partial(edm_marginal_prob, sigma_min=sigma_min, sigma_max=sigma_max)
+        sde_fn = functools.partial(edm_sde, sigma_min=sigma_min, sigma_max=sigma_max)
+        T = sigma_max
+    elif sde_mode == 've':
+        sigma_min = 0.01
+        sigma_max = 50
+        eps = 1e-5
+        marginal_prob_fn = functools.partial(ve_marginal_prob, sigma_min=sigma_min, sigma_max=sigma_max)
+        sde_fn = functools.partial(ve_sde, sigma_min=sigma_min, sigma_max=sigma_max)
+        T = 1.0
+        prior_fn = functools.partial(ve_prior, sigma_min=sigma_min, sigma_max=sigma_max)
+    elif sde_mode == 'vp':
+        beta_0 = 0.1
+        beta_1 = 20
+        eps = 1e-3
+        prior_fn = functools.partial(vp_prior, beta_0=beta_0, beta_1=beta_1)
+        marginal_prob_fn = functools.partial(vp_marginal_prob, beta_0=beta_0, beta_1=beta_1)
+        sde_fn = functools.partial(vp_sde, beta_0=beta_0, beta_1=beta_1)
+        T = 1.0
+    elif sde_mode == 'subvp':
+        beta_0 = 0.1
+        beta_1 = 20
+        eps = 1e-3
+        prior_fn = functools.partial(subvp_prior, beta_0=beta_0, beta_1=beta_1)
+        marginal_prob_fn = functools.partial(subvp_marginal_prob, beta_0=beta_0, beta_1=beta_1)
+        sde_fn = functools.partial(subvp_sde, beta_0=beta_0, beta_1=beta_1)
+        T = 1.0
+    else:
+        raise NotImplementedError
+    return prior_fn, marginal_prob_fn, sde_fn, eps, T

modules/gf_view_finder.py

@@ -0,0 +1,167 @@
+import torch
+import torch.nn as nn
+import PytorchBoot.stereotype as stereotype
+
+from utils.pose import PoseUtil
+import modules.module_lib as mlib
+import modules.func_lib as flib
+
+
+def zero_module(module):
+    """
+    Zero out the parameters of a module and return it.
+    """
+    for p in module.parameters():
+        p.detach().zero_()
+    return module
+
+
+@stereotype.module("gf_view_finder")
+class GradientFieldViewFinder(nn.Module):
+    def __init__(self, config):
+
+        super(GradientFieldViewFinder, self).__init__()
+        
+        self.regression_head = config["regression_head"]
+        self.per_point_feature = config["per_point_feature"]
+        self.act = nn.ReLU(True)
+        self.sample_mode = config["sample_mode"]
+        self.pose_mode = config["pose_mode"]
+        pose_dim = PoseUtil.get_pose_dim(self.pose_mode)
+        self.prior_fn, self.marginal_prob_fn, self.sde_fn, self.sampling_eps, self.T = flib.init_sde(config["sde_mode"])
+        self.sampling_steps = config["sampling_steps"]
+        self.t_feat_dim = config["t_feat_dim"]
+        self.pose_feat_dim = config["pose_feat_dim"]
+        self.main_feat_dim = config["main_feat_dim"]
+
+        ''' encode pose '''
+        self.pose_encoder = nn.Sequential(
+            nn.Linear(pose_dim, self.pose_feat_dim ),
+            self.act,
+            nn.Linear(self.pose_feat_dim , self.pose_feat_dim ),
+            self.act,
+        )
+
+        ''' encode t '''
+        self.t_encoder = nn.Sequential(
+            mlib.GaussianFourierProjection(embed_dim=self.t_feat_dim ),
+            nn.Linear(self.t_feat_dim , self.t_feat_dim ),
+            self.act,
+        )
+
+        ''' fusion tail '''
+        if self.regression_head == 'Rx_Ry_and_T':
+            if self.pose_mode != 'rot_matrix':
+                raise NotImplementedError
+            if not self.per_point_feature:
+                ''' rotation_x_axis regress head '''
+                self.fusion_tail_rot_x = nn.Sequential(
+                    nn.Linear(self.t_feat_dim + self.pose_feat_dim  + self.main_feat_dim, 256),
+                    self.act,
+                    zero_module(nn.Linear(256, 3)),
+                )
+                self.fusion_tail_rot_y = nn.Sequential(
+                    nn.Linear(self.t_feat_dim + self.pose_feat_dim  + self.main_feat_dim, 256),
+                    self.act,
+                    zero_module(nn.Linear(256, 3)),
+                )
+                ''' tranalation regress head '''
+                self.fusion_tail_trans = nn.Sequential(
+                    nn.Linear(self.t_feat_dim + self.pose_feat_dim  + self.main_feat_dim, 256),
+                    self.act,
+                    zero_module(nn.Linear(256, 3)),
+                )  
+            else:
+                raise NotImplementedError
+        else:
+            raise NotImplementedError
+
+    def forward(self, data):
+        """
+        Args:
+            data, dict {
+                'main_feat': [bs, c]
+                'pose_sample': [bs, pose_dim]
+                't': [bs, 1]
+            }
+        """
+
+        main_feat = data['main_feat']
+        sampled_pose = data['sampled_pose']
+        t = data['t']
+        t_feat = self.t_encoder(t.squeeze(1))
+        pose_feat = self.pose_encoder(sampled_pose)
+
+        if self.per_point_feature:
+            raise NotImplementedError
+        else:
+            total_feat = torch.cat([main_feat, t_feat, pose_feat], dim=-1)
+        _, std = self.marginal_prob_fn(total_feat, t)
+
+        if self.regression_head == 'Rx_Ry_and_T':
+            rot_x = self.fusion_tail_rot_x(total_feat)
+            rot_y = self.fusion_tail_rot_y(total_feat)
+            trans = self.fusion_tail_trans(total_feat)
+            out_score = torch.cat([rot_x, rot_y, trans], dim=-1) / (std+1e-7) # normalisation
+        else:
+            raise NotImplementedError
+
+        return out_score
+
+    def marginal_prob(self, x, t):
+        return self.marginal_prob_fn(x,t)
+
+    def sample(self, data, atol=1e-5, rtol=1e-5, snr=0.16, denoise=True, init_x=None, T0=None):
+
+        if self.sample_mode == 'ode':
+            T0 = self.T if T0 is None else T0
+            in_process_sample, res = flib.cond_ode_sampler(
+                score_model=self,
+                data=data,
+                prior=self.prior_fn,
+                sde_coeff=self.sde_fn,
+                atol=atol,
+                rtol=rtol,
+                eps=self.sampling_eps,
+                T=T0,
+                num_steps=self.sampling_steps,
+                pose_mode=self.pose_mode,
+                denoise=denoise,
+                init_x=init_x
+            )
+        else:
+            raise NotImplementedError
+
+        return in_process_sample, res
+
+    def next_best_view(self, main_feat):
+        data = {
+            'main_feat': main_feat,
+        }
+        in_process_sample, res = self.sample(data)
+        return res.to(dtype=torch.float32), in_process_sample
+
+
+''' ----------- DEBUG -----------'''
+if __name__ == "__main__":
+    config = {
+        "regression_head": "Rx_Ry_and_T",
+        "per_point_feature": False,
+        "pose_mode": "rot_matrix",
+        "sde_mode": "ve",
+        "sampling_steps": 500,
+        "sample_mode": "ode"
+    }
+    test_seq_feat = torch.rand(32, 2048).to("cuda:0")
+    test_pose = torch.rand(32, 9).to("cuda:0")
+    test_t = torch.rand(32, 1).to("cuda:0")
+    view_finder = GradientFieldViewFinder(config).to("cuda:0")
+    test_data = {
+        'seq_feat': test_seq_feat,
+        'sampled_pose': test_pose,
+        't': test_t
+    }
+    score = view_finder(test_data)
+    print(score.shape)
+    res, inprocess = view_finder.next_best_view(test_seq_feat)
+    print(res.shape, inprocess.shape)

modules/mlp_view_finder.py

@@ -0,0 +1,91 @@
+import torch
+import torch.nn as nn
+import PytorchBoot.stereotype as stereotype
+
+from utils.pose import PoseUtil
+import modules.module_lib as mlib
+import modules.func_lib as flib
+
+def zero_module(module):
+    """
+    Zero out the parameters of a module and return it.
+    """
+    for p in module.parameters():
+        p.detach().zero_()
+    return module
+
+@stereotype.module("mlp_view_finder")
+class MLPViewFinder(nn.Module):
+    def __init__(self, config):
+
+        super(MLPViewFinder, self).__init__()
+        
+        self.regression_head = 'Rx_Ry_and_T'
+        self.per_point_feature = False
+        self.act = nn.ReLU(True)
+        self.main_feat_dim = config["main_feat_dim"]
+
+        ''' rotation_x_axis regress head '''
+        self.fusion_tail_rot_x = nn.Sequential(
+            nn.Linear(self.main_feat_dim, 256),
+            self.act,
+            zero_module(nn.Linear(256, 3)),
+        )
+        self.fusion_tail_rot_y = nn.Sequential(
+            nn.Linear(self.main_feat_dim, 256),
+            self.act,
+            zero_module(nn.Linear(256, 3)),
+        )
+        ''' tranalation regress head '''
+        self.fusion_tail_trans = nn.Sequential(
+            nn.Linear(self.main_feat_dim, 256),
+            self.act,
+            zero_module(nn.Linear(256, 3)),
+        )  
+    
+
+    def forward(self, data):
+        """
+        Args:
+            data, dict {
+                'main_feat': [bs, c]
+            }
+        """
+
+        total_feat = data['main_feat']
+        rot_x = self.fusion_tail_rot_x(total_feat)
+        rot_y = self.fusion_tail_rot_y(total_feat)
+        trans = self.fusion_tail_trans(total_feat)
+        output = torch.cat([rot_x,rot_y,trans], dim=-1)
+        return output
+
+    def next_best_view(self, main_feat):
+        data = {
+            'main_feat': main_feat,
+        }
+        res = self(data)
+        return res.to(dtype=torch.float32), None
+
+''' ----------- DEBUG -----------'''
+if __name__ == "__main__":
+    config = {
+        "regression_head": "Rx_Ry_and_T",
+        "per_point_feature": False,
+        "pose_mode": "rot_matrix",
+        "sde_mode": "ve",
+        "sampling_steps": 500,
+        "sample_mode": "ode"
+    }
+    test_seq_feat = torch.rand(32, 2048).to("cuda:0")
+    test_pose = torch.rand(32, 9).to("cuda:0")
+    test_t = torch.rand(32, 1).to("cuda:0")
+    view_finder = GradientFieldViewFinder(config).to("cuda:0")
+    test_data = {
+        'seq_feat': test_seq_feat,
+        'sampled_pose': test_pose,
+        't': test_t
+    }
+    score = view_finder(test_data)
+    print(score.shape)
+    res, inprocess = view_finder.next_best_view(test_seq_feat)
+    print(res.shape, inprocess.shape)

modules/module_lib/__init__.py

@@ -0,0 +1,2 @@
+from modules.module_lib.gaussian_fourier_projection import GaussianFourierProjection
+from modules.module_lib.linear import Linear

modules/module_lib/gaussian_fourier_projection.py

@@ -0,0 +1,17 @@
+import torch
+import numpy as np
+import torch.nn as nn
+
+
+class GaussianFourierProjection(nn.Module):
+    """Gaussian random features for encoding time steps."""
+
+    def __init__(self, embed_dim, scale=30.):
+        super().__init__()
+        # Randomly sample weights during initialization. These weights are fixed
+        # during optimization and are not trainable.
+        self.W = nn.Parameter(torch.randn(embed_dim // 2) * scale, requires_grad=False)
+
+    def forward(self, x):
+        x_proj = x[:, None] * self.W[None, :] * 2 * np.pi
+        return torch.cat([torch.sin(x_proj), torch.cos(x_proj)], dim=-1)

modules/module_lib/linear.py

@@ -0,0 +1,30 @@
+import torch
+import numpy as np
+
+
+def weight_init(shape, mode, fan_in, fan_out):
+    if mode == 'xavier_uniform':
+        return np.sqrt(6 / (fan_in + fan_out)) * (torch.rand(*shape) * 2 - 1)
+    if mode == 'xavier_normal':
+        return np.sqrt(2 / (fan_in + fan_out)) * torch.randn(*shape)
+    if mode == 'kaiming_uniform':
+        return np.sqrt(3 / fan_in) * (torch.rand(*shape) * 2 - 1)
+    if mode == 'kaiming_normal':
+        return np.sqrt(1 / fan_in) * torch.randn(*shape)
+    raise ValueError(f'Invalid init mode "{mode}"')
+
+
+class Linear(torch.nn.Module):
+    def __init__(self, in_features, out_features, bias=True, init_mode='kaiming_normal', init_weight=1, init_bias=0):
+        super().__init__()
+        self.in_features = in_features
+        self.out_features = out_features
+        init_kwargs = dict(mode=init_mode, fan_in=in_features, fan_out=out_features)
+        self.weight = torch.nn.Parameter(weight_init([out_features, in_features], **init_kwargs) * init_weight)
+        self.bias = torch.nn.Parameter(weight_init([out_features], **init_kwargs) * init_bias) if bias else None
+
+    def forward(self, x):
+        x = x @ self.weight.to(x.dtype).t()
+        if self.bias is not None:
+            x = x.add_(self.bias.to(x.dtype))
+        return x

modules/module_lib/pointnet2_modules.py

@@ -0,0 +1,162 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from . import pointnet2_utils
+from . import pytorch_utils as pt_utils
+from typing import List
+
+
+class _PointnetSAModuleBase(nn.Module):
+
+    def __init__(self):
+        super().__init__()
+        self.npoint = None
+        self.groupers = None
+        self.mlps = None
+        self.pool_method = 'max_pool'
+
+    def forward(self, xyz: torch.Tensor, features: torch.Tensor = None, new_xyz=None) -> (torch.Tensor, torch.Tensor):
+        """
+        :param xyz: (B, N, 3) tensor of the xyz coordinates of the features
+        :param features: (B, N, C) tensor of the descriptors of the the features
+        :param new_xyz:
+        :return:
+            new_xyz: (B, npoint, 3) tensor of the new features' xyz
+            new_features: (B, npoint, \sum_k(mlps[k][-1])) tensor of the new_features descriptors
+        """
+        new_features_list = []
+
+        xyz_flipped = xyz.transpose(1, 2).contiguous()
+        if new_xyz is None:
+            new_xyz = pointnet2_utils.gather_operation(
+                xyz_flipped,
+                pointnet2_utils.furthest_point_sample(xyz, self.npoint)
+            ).transpose(1, 2).contiguous() if self.npoint is not None else None
+
+        for i in range(len(self.groupers)):
+            new_features = self.groupers[i](xyz, new_xyz, features)  # (B, C, npoint, nsample)
+
+            new_features = self.mlps[i](new_features)  # (B, mlp[-1], npoint, nsample)
+
+            if self.pool_method == 'max_pool':
+                new_features = F.max_pool2d(
+                    new_features, kernel_size=[1, new_features.size(3)]
+                )  # (B, mlp[-1], npoint, 1)
+            elif self.pool_method == 'avg_pool':
+                new_features = F.avg_pool2d(
+                    new_features, kernel_size=[1, new_features.size(3)]
+                )  # (B, mlp[-1], npoint, 1)
+            else:
+                raise NotImplementedError
+
+            new_features = new_features.squeeze(-1)  # (B, mlp[-1], npoint)
+            new_features_list.append(new_features)
+
+        return new_xyz, torch.cat(new_features_list, dim=1)
+
+
+class PointnetSAModuleMSG(_PointnetSAModuleBase):
+    """Pointnet set abstraction layer with multiscale grouping"""
+
+    def __init__(self, *, npoint: int, radii: List[float], nsamples: List[int], mlps: List[List[int]], bn: bool = True,
+                 use_xyz: bool = True, pool_method='max_pool', instance_norm=False):
+        """
+        :param npoint: int
+        :param radii: list of float, list of radii to group with
+        :param nsamples: list of int, number of samples in each ball query
+        :param mlps: list of list of int, spec of the pointnet before the global pooling for each scale
+        :param bn: whether to use batchnorm
+        :param use_xyz:
+        :param pool_method: max_pool / avg_pool
+        :param instance_norm: whether to use instance_norm
+        """
+        super().__init__()
+
+        assert len(radii) == len(nsamples) == len(mlps)
+
+        self.npoint = npoint
+        self.groupers = nn.ModuleList()
+        self.mlps = nn.ModuleList()
+        for i in range(len(radii)):
+            radius = radii[i]
+            nsample = nsamples[i]
+            self.groupers.append(
+                pointnet2_utils.QueryAndGroup(radius, nsample, use_xyz=use_xyz)
+                if npoint is not None else pointnet2_utils.GroupAll(use_xyz)
+            )
+            mlp_spec = mlps[i]
+            if use_xyz:
+                mlp_spec[0] += 3
+
+            self.mlps.append(pt_utils.SharedMLP(mlp_spec, bn=bn, instance_norm=instance_norm))
+        self.pool_method = pool_method
+
+
+class PointnetSAModule(PointnetSAModuleMSG):
+    """Pointnet set abstraction layer"""
+
+    def __init__(self, *, mlp: List[int], npoint: int = None, radius: float = None, nsample: int = None,
+                 bn: bool = True, use_xyz: bool = True, pool_method='max_pool', instance_norm=False):
+        """
+        :param mlp: list of int, spec of the pointnet before the global max_pool
+        :param npoint: int, number of features
+        :param radius: float, radius of ball
+        :param nsample: int, number of samples in the ball query
+        :param bn: whether to use batchnorm
+        :param use_xyz:
+        :param pool_method: max_pool / avg_pool
+        :param instance_norm: whether to use instance_norm
+        """
+        super().__init__(
+            mlps=[mlp], npoint=npoint, radii=[radius], nsamples=[nsample], bn=bn, use_xyz=use_xyz,
+            pool_method=pool_method, instance_norm=instance_norm
+        )
+
+
+class PointnetFPModule(nn.Module):
+    r"""Propigates the features of one set to another"""
+
+    def __init__(self, *, mlp: List[int], bn: bool = True):
+        """
+        :param mlp: list of int
+        :param bn: whether to use batchnorm
+        """
+        super().__init__()
+        self.mlp = pt_utils.SharedMLP(mlp, bn=bn)
+
+    def forward(
+            self, unknown: torch.Tensor, known: torch.Tensor, unknow_feats: torch.Tensor, known_feats: torch.Tensor
+    ) -> torch.Tensor:
+        """
+        :param unknown: (B, n, 3) tensor of the xyz positions of the unknown features
+        :param known: (B, m, 3) tensor of the xyz positions of the known features
+        :param unknow_feats: (B, C1, n) tensor of the features to be propigated to
+        :param known_feats: (B, C2, m) tensor of features to be propigated
+        :return:
+            new_features: (B, mlp[-1], n) tensor of the features of the unknown features
+        """
+        if known is not None:
+            dist, idx = pointnet2_utils.three_nn(unknown, known)
+            dist_recip = 1.0 / (dist + 1e-8)
+            norm = torch.sum(dist_recip, dim=2, keepdim=True)
+            weight = dist_recip / norm
+
+            interpolated_feats = pointnet2_utils.three_interpolate(known_feats, idx, weight)
+        else:
+            interpolated_feats = known_feats.expand(*known_feats.size()[0:2], unknown.size(1))
+
+        if unknow_feats is not None:
+            new_features = torch.cat([interpolated_feats, unknow_feats], dim=1)  # (B, C2 + C1, n)
+        else:
+            new_features = interpolated_feats
+
+        new_features = new_features.unsqueeze(-1)
+
+        new_features = self.mlp(new_features)
+
+        return new_features.squeeze(-1)
+
+
+if __name__ == "__main__":
+    pass

modules/module_lib/pointnet2_utils.py

@@ -0,0 +1,291 @@
+import torch
+from torch.autograd import Variable
+from torch.autograd import Function
+import torch.nn as nn
+from typing import Tuple
+import sys
+
+import pointnet2_cuda as pointnet2
+
+
+class FurthestPointSampling(Function):
+    @staticmethod
+    def forward(ctx, xyz: torch.Tensor, npoint: int) -> torch.Tensor:
+        """
+        Uses iterative furthest point sampling to select a set of npoint features that have the largest
+        minimum distance
+        :param ctx:
+        :param xyz: (B, N, 3) where N > npoint
+        :param npoint: int, number of features in the sampled set
+        :return:
+             output: (B, npoint) tensor containing the set
+        """
+        assert xyz.is_contiguous()
+
+        B, N, _ = xyz.size()
+        output = torch.cuda.IntTensor(B, npoint)
+        temp = torch.cuda.FloatTensor(B, N).fill_(1e10)
+
+        pointnet2.furthest_point_sampling_wrapper(B, N, npoint, xyz, temp, output)
+        return output
+
+    @staticmethod
+    def backward(xyz, a=None):
+        return None, None
+
+
+furthest_point_sample = FurthestPointSampling.apply
+
+
+class GatherOperation(Function):
+
+    @staticmethod
+    def forward(ctx, features: torch.Tensor, idx: torch.Tensor) -> torch.Tensor:
+        """
+        :param ctx:
+        :param features: (B, C, N)
+        :param idx: (B, npoint) index tensor of the features to gather
+        :return:
+            output: (B, C, npoint)
+        """
+        assert features.is_contiguous()
+        assert idx.is_contiguous()
+
+        B, npoint = idx.size()
+        _, C, N = features.size()
+        output = torch.cuda.FloatTensor(B, C, npoint)
+
+        pointnet2.gather_points_wrapper(B, C, N, npoint, features, idx, output)
+
+        ctx.for_backwards = (idx, C, N)
+        return output
+
+    @staticmethod
+    def backward(ctx, grad_out):
+        idx, C, N = ctx.for_backwards
+        B, npoint = idx.size()
+
+        grad_features = Variable(torch.cuda.FloatTensor(B, C, N).zero_())
+        grad_out_data = grad_out.data.contiguous()
+        pointnet2.gather_points_grad_wrapper(B, C, N, npoint, grad_out_data, idx, grad_features.data)
+        return grad_features, None
+
+
+gather_operation = GatherOperation.apply
+
+
+class ThreeNN(Function):
+
+    @staticmethod
+    def forward(ctx, unknown: torch.Tensor, known: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        Find the three nearest neighbors of unknown in known
+        :param ctx:
+        :param unknown: (B, N, 3)
+        :param known: (B, M, 3)
+        :return:
+            dist: (B, N, 3) l2 distance to the three nearest neighbors
+            idx: (B, N, 3) index of 3 nearest neighbors
+        """
+        assert unknown.is_contiguous()
+        assert known.is_contiguous()
+
+        B, N, _ = unknown.size()
+        m = known.size(1)
+        dist2 = torch.cuda.FloatTensor(B, N, 3)
+        idx = torch.cuda.IntTensor(B, N, 3)
+
+        pointnet2.three_nn_wrapper(B, N, m, unknown, known, dist2, idx)
+        return torch.sqrt(dist2), idx
+
+    @staticmethod
+    def backward(ctx, a=None, b=None):
+        return None, None
+
+
+three_nn = ThreeNN.apply
+
+
+class ThreeInterpolate(Function):
+
+    @staticmethod
+    def forward(ctx, features: torch.Tensor, idx: torch.Tensor, weight: torch.Tensor) -> torch.Tensor:
+        """
+        Performs weight linear interpolation on 3 features
+        :param ctx:
+        :param features: (B, C, M) Features descriptors to be interpolated from
+        :param idx: (B, n, 3) three nearest neighbors of the target features in features
+        :param weight: (B, n, 3) weights
+        :return:
+            output: (B, C, N) tensor of the interpolated features
+        """
+        assert features.is_contiguous()
+        assert idx.is_contiguous()
+        assert weight.is_contiguous()
+
+        B, c, m = features.size()
+        n = idx.size(1)
+        ctx.three_interpolate_for_backward = (idx, weight, m)
+        output = torch.cuda.FloatTensor(B, c, n)
+
+        pointnet2.three_interpolate_wrapper(B, c, m, n, features, idx, weight, output)
+        return output
+
+    @staticmethod
+    def backward(ctx, grad_out: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        """
+        :param ctx:
+        :param grad_out: (B, C, N) tensor with gradients of outputs
+        :return:
+            grad_features: (B, C, M) tensor with gradients of features
+            None:
+            None:
+        """
+        idx, weight, m = ctx.three_interpolate_for_backward
+        B, c, n = grad_out.size()
+
+        grad_features = Variable(torch.cuda.FloatTensor(B, c, m).zero_())
+        grad_out_data = grad_out.data.contiguous()
+
+        pointnet2.three_interpolate_grad_wrapper(B, c, n, m, grad_out_data, idx, weight, grad_features.data)
+        return grad_features, None, None
+
+
+three_interpolate = ThreeInterpolate.apply
+
+
+class GroupingOperation(Function):
+
+    @staticmethod
+    def forward(ctx, features: torch.Tensor, idx: torch.Tensor) -> torch.Tensor:
+        """
+        :param ctx:
+        :param features: (B, C, N) tensor of features to group
+        :param idx: (B, npoint, nsample) tensor containing the indicies of features to group with
+        :return:
+            output: (B, C, npoint, nsample) tensor
+        """
+        assert features.is_contiguous()
+        assert idx.is_contiguous()
+
+        B, nfeatures, nsample = idx.size()
+        _, C, N = features.size()
+        output = torch.cuda.FloatTensor(B, C, nfeatures, nsample)
+
+        pointnet2.group_points_wrapper(B, C, N, nfeatures, nsample, features, idx, output)
+
+        ctx.for_backwards = (idx, N)
+        return output
+
+    @staticmethod
+    def backward(ctx, grad_out: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        :param ctx:
+        :param grad_out: (B, C, npoint, nsample) tensor of the gradients of the output from forward
+        :return:
+            grad_features: (B, C, N) gradient of the features
+        """
+        idx, N = ctx.for_backwards
+
+        B, C, npoint, nsample = grad_out.size()
+        grad_features = Variable(torch.cuda.FloatTensor(B, C, N).zero_())
+
+        grad_out_data = grad_out.data.contiguous()
+        pointnet2.group_points_grad_wrapper(B, C, N, npoint, nsample, grad_out_data, idx, grad_features.data)
+        return grad_features, None
+
+
+grouping_operation = GroupingOperation.apply
+
+
+class BallQuery(Function):
+
+    @staticmethod
+    def forward(ctx, radius: float, nsample: int, xyz: torch.Tensor, new_xyz: torch.Tensor) -> torch.Tensor:
+        """
+        :param ctx:
+        :param radius: float, radius of the balls
+        :param nsample: int, maximum number of features in the balls
+        :param xyz: (B, N, 3) xyz coordinates of the features
+        :param new_xyz: (B, npoint, 3) centers of the ball query
+        :return:
+            idx: (B, npoint, nsample) tensor with the indicies of the features that form the query balls
+        """
+        assert new_xyz.is_contiguous()
+        assert xyz.is_contiguous()
+
+        B, N, _ = xyz.size()
+        npoint = new_xyz.size(1)
+        idx = torch.cuda.IntTensor(B, npoint, nsample).zero_()
+
+        pointnet2.ball_query_wrapper(B, N, npoint, radius, nsample, new_xyz, xyz, idx)
+        return idx
+
+    @staticmethod
+    def backward(ctx, a=None):
+        return None, None, None, None
+
+
+ball_query = BallQuery.apply
+
+
+class QueryAndGroup(nn.Module):
+    def __init__(self, radius: float, nsample: int, use_xyz: bool = True):
+        """
+        :param radius: float, radius of ball
+        :param nsample: int, maximum number of features to gather in the ball
+        :param use_xyz:
+        """
+        super().__init__()
+        self.radius, self.nsample, self.use_xyz = radius, nsample, use_xyz
+
+    def forward(self, xyz: torch.Tensor, new_xyz: torch.Tensor, features: torch.Tensor = None) -> Tuple[torch.Tensor]:
+        """
+        :param xyz: (B, N, 3) xyz coordinates of the features
+        :param new_xyz: (B, npoint, 3) centroids
+        :param features: (B, C, N) descriptors of the features
+        :return:
+            new_features: (B, 3 + C, npoint, nsample)
+        """
+        idx = ball_query(self.radius, self.nsample, xyz, new_xyz)
+        xyz_trans = xyz.transpose(1, 2).contiguous()
+        grouped_xyz = grouping_operation(xyz_trans, idx)  # (B, 3, npoint, nsample)
+        grouped_xyz -= new_xyz.transpose(1, 2).unsqueeze(-1)
+
+        if features is not None:
+            grouped_features = grouping_operation(features, idx)
+            if self.use_xyz:
+                new_features = torch.cat([grouped_xyz, grouped_features], dim=1)  # (B, C + 3, npoint, nsample)
+            else:
+                new_features = grouped_features
+        else:
+            assert self.use_xyz, "Cannot have not features and not use xyz as a feature!"
+            new_features = grouped_xyz
+
+        return new_features
+
+
+class GroupAll(nn.Module):
+    def __init__(self, use_xyz: bool = True):
+        super().__init__()
+        self.use_xyz = use_xyz
+
+    def forward(self, xyz: torch.Tensor, new_xyz: torch.Tensor, features: torch.Tensor = None):
+        """
+        :param xyz: (B, N, 3) xyz coordinates of the features
+        :param new_xyz: ignored
+        :param features: (B, C, N) descriptors of the features
+        :return:
+            new_features: (B, C + 3, 1, N)
+        """
+        grouped_xyz = xyz.transpose(1, 2).unsqueeze(2)
+        if features is not None:
+            grouped_features = features.unsqueeze(2)
+            if self.use_xyz:
+                new_features = torch.cat([grouped_xyz, grouped_features], dim=1)  # (B, 3 + C, 1, N)
+            else:
+                new_features = grouped_features
+        else:
+            new_features = grouped_xyz
+
+        return new_features

modules/module_lib/pytorch_utils.py

@@ -0,0 +1,236 @@
+import torch.nn as nn
+from typing import List, Tuple
+
+
+class SharedMLP(nn.Sequential):
+
+    def __init__(
+            self,
+            args: List[int],
+            *,
+            bn: bool = False,
+            activation=nn.ReLU(inplace=True),
+            preact: bool = False,
+            first: bool = False,
+            name: str = "",
+            instance_norm: bool = False,
+    ):
+        super().__init__()
+
+        for i in range(len(args) - 1):
+            self.add_module(
+                name + 'layer{}'.format(i),
+                Conv2d(
+                    args[i],
+                    args[i + 1],
+                    bn=(not first or not preact or (i != 0)) and bn,
+                    activation=activation
+                    if (not first or not preact or (i != 0)) else None,
+                    preact=preact,
+                    instance_norm=instance_norm
+                )
+            )
+
+
+class _ConvBase(nn.Sequential):
+
+    def __init__(
+            self,
+            in_size,
+            out_size,
+            kernel_size,
+            stride,
+            padding,
+            activation,
+            bn,
+            init,
+            conv=None,
+            batch_norm=None,
+            bias=True,
+            preact=False,
+            name="",
+            instance_norm=False,
+            instance_norm_func=None
+    ):
+        super().__init__()
+
+        bias = bias and (not bn)
+        conv_unit = conv(
+            in_size,
+            out_size,
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=padding,
+            bias=bias
+        )
+        init(conv_unit.weight)
+        if bias:
+            nn.init.constant_(conv_unit.bias, 0)
+
+        if bn:
+            if not preact:
+                bn_unit = batch_norm(out_size)
+            else:
+                bn_unit = batch_norm(in_size)
+        if instance_norm:
+            if not preact:
+                in_unit = instance_norm_func(out_size, affine=False, track_running_stats=False)
+            else:
+                in_unit = instance_norm_func(in_size, affine=False, track_running_stats=False)
+
+        if preact:
+            if bn:
+                self.add_module(name + 'bn', bn_unit)
+
+            if activation is not None:
+                self.add_module(name + 'activation', activation)
+
+            if not bn and instance_norm:
+                self.add_module(name + 'in', in_unit)
+
+        self.add_module(name + 'conv', conv_unit)
+
+        if not preact:
+            if bn:
+                self.add_module(name + 'bn', bn_unit)
+
+            if activation is not None:
+                self.add_module(name + 'activation', activation)
+
+            if not bn and instance_norm:
+                self.add_module(name + 'in', in_unit)
+
+
+class _BNBase(nn.Sequential):
+
+    def __init__(self, in_size, batch_norm=None, name=""):
+        super().__init__()
+        self.add_module(name + "bn", batch_norm(in_size))
+
+        nn.init.constant_(self[0].weight, 1.0)
+        nn.init.constant_(self[0].bias, 0)
+
+
+class BatchNorm1d(_BNBase):
+
+    def __init__(self, in_size: int, *, name: str = ""):
+        super().__init__(in_size, batch_norm=nn.BatchNorm1d, name=name)
+
+
+class BatchNorm2d(_BNBase):
+
+    def __init__(self, in_size: int, name: str = ""):
+        super().__init__(in_size, batch_norm=nn.BatchNorm2d, name=name)
+
+
+class Conv1d(_ConvBase):
+
+    def __init__(
+            self,
+            in_size: int,
+            out_size: int,
+            *,
+            kernel_size: int = 1,
+            stride: int = 1,
+            padding: int = 0,
+            activation=nn.ReLU(inplace=True),
+            bn: bool = False,
+            init=nn.init.kaiming_normal_,
+            bias: bool = True,
+            preact: bool = False,
+            name: str = "",
+            instance_norm=False
+    ):
+        super().__init__(
+            in_size,
+            out_size,
+            kernel_size,
+            stride,
+            padding,
+            activation,
+            bn,
+            init,
+            conv=nn.Conv1d,
+            batch_norm=BatchNorm1d,
+            bias=bias,
+            preact=preact,
+            name=name,
+            instance_norm=instance_norm,
+            instance_norm_func=nn.InstanceNorm1d
+        )
+
+
+class Conv2d(_ConvBase):
+
+    def __init__(
+            self,
+            in_size: int,
+            out_size: int,
+            *,
+            kernel_size: Tuple[int, int] = (1, 1),
+            stride: Tuple[int, int] = (1, 1),
+            padding: Tuple[int, int] = (0, 0),
+            activation=nn.ReLU(inplace=True),
+            bn: bool = False,
+            init=nn.init.kaiming_normal_,
+            bias: bool = True,
+            preact: bool = False,
+            name: str = "",
+            instance_norm=False
+    ):
+        super().__init__(
+            in_size,
+            out_size,
+            kernel_size,
+            stride,
+            padding,
+            activation,
+            bn,
+            init,
+            conv=nn.Conv2d,
+            batch_norm=BatchNorm2d,
+            bias=bias,
+            preact=preact,
+            name=name,
+            instance_norm=instance_norm,
+            instance_norm_func=nn.InstanceNorm2d
+        )
+
+
+class FC(nn.Sequential):
+
+    def __init__(
+            self,
+            in_size: int,
+            out_size: int,
+            *,
+            activation=nn.ReLU(inplace=True),
+            bn: bool = False,
+            init=None,
+            preact: bool = False,
+            name: str = ""
+    ):
+        super().__init__()
+
+        fc = nn.Linear(in_size, out_size, bias=not bn)
+        if init is not None:
+            init(fc.weight)
+        if not bn:
+            nn.init.constant(fc.bias, 0)
+
+        if preact:
+            if bn:
+                self.add_module(name + 'bn', BatchNorm1d(in_size))
+
+            if activation is not None:
+                self.add_module(name + 'activation', activation)
+
+        self.add_module(name + 'fc', fc)
+
+        if not preact:
+            if bn:
+                self.add_module(name + 'bn', BatchNorm1d(out_size))
+
+            if activation is not None:
+                self.add_module(name + 'activation', activation)
+

modules/pointnet++_encoder.py

@@ -0,0 +1,149 @@
+import torch
+import torch.nn as nn
+import os
+import sys
+path = os.path.abspath(__file__)
+for i in range(2):
+    path = os.path.dirname(path)
+PROJECT_ROOT = path                 
+sys.path.append(PROJECT_ROOT)
+import PytorchBoot.stereotype as stereotype
+from modules.module_lib.pointnet2_modules import PointnetSAModuleMSG
+
+
+ClsMSG_CFG_Dense = {
+    'NPOINTS': [512, 256, 128, None],
+    'RADIUS': [[0.02, 0.04], [0.04, 0.08], [0.08, 0.16], [None, None]],
+    'NSAMPLE': [[32, 64], [16, 32], [8, 16], [None, None]],
+    'MLPS': [[[16, 16, 32], [32, 32, 64]],
+             [[64, 64, 128], [64, 96, 128]],
+             [[128, 196, 256], [128, 196, 256]],
+             [[256, 256, 512], [256, 384, 512]]],
+    'DP_RATIO': 0.5,
+}
+
+ClsMSG_CFG_Light = {
+    'NPOINTS': [512, 256, 128, None],
+    'RADIUS': [[0.02, 0.04], [0.04, 0.08], [0.08, 0.16], [None, None]],
+    'NSAMPLE': [[16, 32], [16, 32], [16, 32], [None, None]],
+    'MLPS': [[[16, 16, 32], [32, 32, 64]],
+             [[64, 64, 128], [64, 96, 128]],
+             [[128, 196, 256], [128, 196, 256]],
+             [[256, 256, 512], [256, 384, 512]]],
+    'DP_RATIO': 0.5,
+}
+
+ClsMSG_CFG_Light_2048 = {
+    'NPOINTS': [512, 256, 128, None],
+    'RADIUS': [[0.02, 0.04], [0.04, 0.08], [0.08, 0.16], [None, None]],
+    'NSAMPLE': [[16, 32], [16, 32], [16, 32], [None, None]],
+    'MLPS': [[[16, 16, 32], [32, 32, 64]],
+         [[64, 64, 128], [64, 96, 128]],
+         [[128, 196, 256], [128, 196, 256]],
+         [[256, 256, 1024], [256, 512, 1024]]],
+    'DP_RATIO': 0.5,
+}
+
+ClsMSG_CFG_Strong = {
+    'NPOINTS': [512, 256, 128, 64, None],
+    'RADIUS': [[0.02, 0.04], [0.04, 0.08], [0.08, 0.16],[0.16, 0.32], [None, None]],
+    'NSAMPLE': [[16, 32], [16, 32], [16, 32], [16, 32], [None, None]],
+    'MLPS': [[[16, 16, 32], [32, 32, 64]],
+         [[64, 64, 128], [64, 96, 128]],
+         [[128, 196, 256], [128, 196, 256]],
+         [[256, 256, 512], [256, 512, 512]],
+         [[512, 512, 2048], [512, 1024, 2048]]
+         ],
+    'DP_RATIO': 0.5,
+}
+
+ClsMSG_CFG_Lighter = {
+    'NPOINTS': [512, 256, 128, 64, None],
+    'RADIUS': [[0.01], [0.02], [0.04], [0.08], [None]],
+    'NSAMPLE': [[64], [32], [16], [8], [None]],
+    'MLPS': [[[32, 32, 64]],
+             [[64, 64, 128]],
+             [[128, 196, 256]],
+             [[256, 256, 512]],
+             [[512, 512, 1024]]],
+    'DP_RATIO': 0.5,
+}
+
+
+def select_params(name):
+    if name == 'light':
+        return ClsMSG_CFG_Light
+    elif name == 'lighter':
+        return ClsMSG_CFG_Lighter
+    elif name == 'dense':
+        return ClsMSG_CFG_Dense
+    elif name == 'light_2048':
+        return ClsMSG_CFG_Light_2048
+    elif name == 'strong':
+        return ClsMSG_CFG_Strong
+    else:
+        raise NotImplementedError
+
+
+def break_up_pc(pc):
+    xyz = pc[..., 0:3].contiguous()
+    features = (
+        pc[..., 3:].transpose(1, 2).contiguous()
+        if pc.size(-1) > 3 else None
+    )
+
+    return xyz, features
+
+
+@stereotype.module("pointnet++_encoder")
+class PointNet2Encoder(nn.Module):
+    def encode_points(self, pts, require_per_point_feat=False):
+        return self.forward(pts)
+
+    def __init__(self, config:dict):
+        super().__init__()
+
+        channel_in = config.get("in_dim", 3) - 3
+        params_name = config.get("params_name", "light")
+
+        self.SA_modules = nn.ModuleList()
+        selected_params = select_params(params_name)
+        for k in range(selected_params['NPOINTS'].__len__()):
+            mlps = selected_params['MLPS'][k].copy()
+            channel_out = 0
+            for idx in range(mlps.__len__()):
+                mlps[idx] = [channel_in] + mlps[idx]
+                channel_out += mlps[idx][-1]
+
+            self.SA_modules.append(
+                PointnetSAModuleMSG(
+                    npoint=selected_params['NPOINTS'][k],
+                    radii=selected_params['RADIUS'][k],
+                    nsamples=selected_params['NSAMPLE'][k],
+                    mlps=mlps,
+                    use_xyz=True,
+                    bn=True
+                )
+            )
+            channel_in = channel_out
+
+    def forward(self, point_cloud: torch.cuda.FloatTensor):
+        xyz, features = break_up_pc(point_cloud)
+
+        l_xyz, l_features = [xyz], [features]
+        for i in range(len(self.SA_modules)):
+            li_xyz, li_features = self.SA_modules[i](l_xyz[i], l_features[i])
+            l_xyz.append(li_xyz)
+            l_features.append(li_features)
+        return l_features[-1].squeeze(-1)
+
+
+if __name__ == '__main__':
+    seed = 100
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed(seed)
+    net = PointNet2Encoder(config={"in_dim": 3, "params_name": "strong"}).cuda()
+    pts = torch.randn(2, 2444, 3).cuda()
+    print(torch.mean(pts, dim=1))
+    pre = net.encode_points(pts)
+    print(pre.shape)

modules/pointnet_encoder.py

@@ -0,0 +1,107 @@
+from __future__ import print_function
+import torch
+import torch.nn as nn
+import torch.nn.parallel
+import torch.utils.data
+from torch.autograd import Variable
+import numpy as np
+import torch.nn.functional as F
+
+import PytorchBoot.stereotype as stereotype
+@stereotype.module("pointnet_encoder")
+class PointNetEncoder(nn.Module):
+
+    def __init__(self, config:dict):
+        super(PointNetEncoder, self).__init__()
+        
+        self.out_dim = config["out_dim"]
+        self.in_dim = config["in_dim"]
+        self.feature_transform = config.get("feature_transform", False)
+        self.stn = STNkd(k=self.in_dim)
+        self.conv1 = torch.nn.Conv1d(self.in_dim , 64, 1)
+        self.conv2 = torch.nn.Conv1d(64, 128, 1)
+        self.conv3 = torch.nn.Conv1d(128, 512, 1)
+        self.conv4 = torch.nn.Conv1d(512, self.out_dim , 1)
+        if self.feature_transform:
+            self.f_stn = STNkd(k=64)
+
+    def forward(self, x):
+        trans = self.stn(x)
+        x = x.transpose(2, 1)
+        x = torch.bmm(x, trans)
+        x = x.transpose(2, 1)
+        x = F.relu(self.conv1(x))
+
+        if self.feature_transform:
+            trans_feat = self.f_stn(x)
+            x = x.transpose(2, 1)
+            x = torch.bmm(x, trans_feat)
+            x = x.transpose(2, 1)
+
+        point_feat = x
+        x = F.relu(self.conv2(x))
+        x = F.relu(self.conv3(x))
+        x = self.conv4(x)
+        x = torch.max(x, 2, keepdim=True)[0]
+        x = x.view(-1, self.out_dim)
+        return x, point_feat
+
+    def encode_points(self, pts, require_per_point_feat=False):
+        pts = pts.transpose(2, 1)
+        global_pts_feature, per_point_feature = self(pts)
+        if require_per_point_feat:
+            return global_pts_feature, per_point_feature.transpose(2, 1)
+        else:
+            return global_pts_feature
+
+class STNkd(nn.Module):
+    def __init__(self, k=64):
+        super(STNkd, self).__init__()
+        self.conv1 = torch.nn.Conv1d(k, 64, 1)
+        self.conv2 = torch.nn.Conv1d(64, 128, 1)
+        self.conv3 = torch.nn.Conv1d(128, 1024, 1)
+        self.fc1 = nn.Linear(1024, 512)
+        self.fc2 = nn.Linear(512, 256)
+        self.fc3 = nn.Linear(256, k * k)
+        self.relu = nn.ReLU()
+
+        self.k = k
+
+    def forward(self, x):
+        batchsize = x.size()[0]
+        x = F.relu(self.conv1(x))
+        x = F.relu(self.conv2(x))
+        x = F.relu(self.conv3(x))
+        x = torch.max(x, 2, keepdim=True)[0]
+        x = x.view(-1, 1024)
+
+        x = F.relu(self.fc1(x))
+        x = F.relu(self.fc2(x))
+        x = self.fc3(x)
+
+        iden = (
+            Variable(torch.from_numpy(np.eye(self.k).flatten().astype(np.float32)))
+            .view(1, self.k * self.k)
+            .repeat(batchsize, 1)
+        )
+        if x.is_cuda:
+            iden = iden.to(x.get_device())
+        x = x + iden
+        x = x.view(-1, self.k, self.k)
+        return x
+
+if __name__ == "__main__":
+    sim_data = Variable(torch.rand(32, 2500, 3))
+    config = {
+        "in_dim": 3,
+        "out_dim": 1024,
+        "feature_transform": False
+    }
+    pointnet = PointNetEncoder(config)
+    out = pointnet.encode_points(sim_data)
+
+    print("global feat", out.size())
+
+    out, per_point_out = pointnet.encode_points(sim_data, require_per_point_feat=True)
+    print("point feat", out.size())
+    print("per point feat", per_point_out.size())

modules/pose_encoder.py

@@ -0,0 +1,21 @@
+from torch import nn
+import PytorchBoot.stereotype as stereotype
+
+@stereotype.module("pose_encoder")
+class PoseEncoder(nn.Module):
+    def __init__(self, config):
+        super(PoseEncoder, self).__init__()
+        self.config = config
+        pose_dim = config["pose_dim"]
+        out_dim = config["out_dim"]
+        self.act = nn.ReLU(True)
+
+        self.pose_encoder = nn.Sequential(
+            nn.Linear(pose_dim, out_dim),
+            self.act,
+            nn.Linear(out_dim, out_dim),
+            self.act,
+        )
+
+    def encode_pose(self, pose):
+        return self.pose_encoder(pose)

modules/pts_num_encoder.py

@@ -0,0 +1,20 @@
+from torch import nn
+import PytorchBoot.stereotype as stereotype
+
+@stereotype.module("pts_num_encoder")
+class PointsNumEncoder(nn.Module):
+    def __init__(self, config):
+        super(PointsNumEncoder, self).__init__()
+        self.config = config
+        out_dim = config["out_dim"]
+        self.act = nn.ReLU(True)
+
+        self.pts_num_encoder = nn.Sequential(
+            nn.Linear(1, out_dim),
+            self.act,
+            nn.Linear(out_dim, out_dim),
+            self.act,
+        )
+
+    def encode_pts_num(self, num_seq):
+        return self.pts_num_encoder(num_seq)

modules/transformer_seq_encoder.py

@@ -0,0 +1,63 @@
+import torch
+from torch import nn
+from torch.nn.utils.rnn import pad_sequence
+import PytorchBoot.stereotype as stereotype
+
+
+@stereotype.module("transformer_seq_encoder")
+class TransformerSequenceEncoder(nn.Module):
+    def __init__(self, config):
+        super(TransformerSequenceEncoder, self).__init__()
+        self.config = config
+        embed_dim = config["embed_dim"]
+        encoder_layer = nn.TransformerEncoderLayer(
+            d_model=embed_dim,
+            nhead=config["num_heads"],
+            dim_feedforward=config["ffn_dim"],
+            batch_first=True,
+        )
+        self.transformer_encoder = nn.TransformerEncoder(
+            encoder_layer, num_layers=config["num_layers"]
+        )
+        self.fc = nn.Linear(embed_dim, config["output_dim"])
+
+    def encode_sequence(self, embedding_list_batch):
+        
+        lengths = []
+        
+        for embedding_list in embedding_list_batch:
+            lengths.append(len(embedding_list))
+
+        embedding_tensor = pad_sequence(embedding_list_batch, batch_first=True)  # Shape: [batch_size, max_seq_len, embed_dim]
+
+        max_len = max(lengths)
+        padding_mask = torch.tensor([([0] * length + [1] * (max_len - length)) for length in lengths], dtype=torch.bool).to(embedding_tensor.device)
+
+        transformer_output = self.transformer_encoder(embedding_tensor, src_key_padding_mask=padding_mask)
+        final_feature = transformer_output.mean(dim=1)
+        final_output = self.fc(final_feature)
+
+        return final_output
+
+
+if __name__ == "__main__":
+    config = {
+        "embed_dim": 256,
+        "num_heads": 4,  
+        "ffn_dim": 256,
+        "num_layers": 3, 
+        "output_dim": 1024, 
+    }
+
+    encoder = TransformerSequenceEncoder(config)
+    seq_len = [5, 8, 9, 4]
+    batch_size = 4
+
+    embedding_list_batch = [
+        torch.randn(seq_len[idx], config["embed_dim"]) for idx in range(batch_size)
+    ]
+    output_feature = encoder.encode_sequence(
+        embedding_list_batch
+    )
+    print("Encoded Feature:", output_feature)
+    print("Feature Shape:", output_feature.shape)

preprocess/clean_preprocessed_data.py

@@ -0,0 +1,43 @@
+import os
+import shutil
+
+def clean_scene_data(root, scene):
+    # 清理目标点云数据
+    pts_dir = os.path.join(root, scene, "pts")
+    if os.path.exists(pts_dir):
+        shutil.rmtree(pts_dir)
+        print(f"已删除 {pts_dir}")
+
+    # 清理法线数据
+    nrm_dir = os.path.join(root, scene, "nrm")
+    if os.path.exists(nrm_dir):
+        shutil.rmtree(nrm_dir)
+        print(f"已删除 {nrm_dir}")
+
+    # 清理扫描点索引数据
+    scan_points_indices_dir = os.path.join(root, scene, "scan_points_indices")
+    if os.path.exists(scan_points_indices_dir):
+        shutil.rmtree(scan_points_indices_dir)
+        print(f"已删除 {scan_points_indices_dir}")
+
+    # 删除扫描点数据文件
+    scan_points_file = os.path.join(root, scene, "scan_points.txt")
+    if os.path.exists(scan_points_file):
+        os.remove(scan_points_file)
+        print(f"已删除 {scan_points_file}")
+
+def clean_all_scenes(root, scene_list):
+    for idx, scene in enumerate(scene_list):
+        print(f"正在清理场景 {scene} ({idx+1}/{len(scene_list)})")
+        clean_scene_data(root, scene)
+
+if __name__ == "__main__":
+    root = r"c:\Document\Local Project\nbv_rec\nbv_reconstruction\temp"
+    scene_list = os.listdir(root)
+    from_idx = 0
+    to_idx = len(scene_list)
+    print(f"正在清理场景 {scene_list[from_idx:to_idx]}")
+
+    clean_all_scenes(root, scene_list[from_idx:to_idx])
+    print("清理完成")
+

preprocess/pack_preprocessed_data.py

@@ -0,0 +1,48 @@
+import os
+import shutil
+
+def pack_scene_data(root, scene, output_dir):
+    scene_dir = os.path.join(output_dir, scene)
+    if not os.path.exists(scene_dir):
+        os.makedirs(scene_dir)
+
+    pts_dir = os.path.join(root, scene, "pts")
+    if os.path.exists(pts_dir):
+        shutil.move(pts_dir, os.path.join(scene_dir, "pts"))
+        
+    scan_points_indices_dir = os.path.join(root, scene, "scan_points_indices")
+    if os.path.exists(scan_points_indices_dir):
+        shutil.move(scan_points_indices_dir, os.path.join(scene_dir, "scan_points_indices")) 
+
+    scan_points_file = os.path.join(root, scene, "scan_points.txt")
+    if os.path.exists(scan_points_file):
+        shutil.move(scan_points_file, os.path.join(scene_dir, "scan_points.txt"))
+
+    model_pts_nrm_file = os.path.join(root, scene, "points_and_normals.txt")
+    if os.path.exists(model_pts_nrm_file):
+        shutil.move(model_pts_nrm_file, os.path.join(scene_dir, "points_and_normals.txt"))
+
+    camera_dir = os.path.join(root, scene, "camera_params")
+    if os.path.exists(camera_dir):
+        shutil.move(camera_dir, os.path.join(scene_dir, "camera_params"))
+
+    scene_info_file = os.path.join(root, scene, "scene_info.json")
+    if os.path.exists(scene_info_file):
+        shutil.move(scene_info_file, os.path.join(scene_dir, "scene_info.json"))
+
+def pack_all_scenes(root, scene_list, output_dir):
+    for idx, scene in enumerate(scene_list):
+        print(f"正在打包场景 {scene} ({idx+1}/{len(scene_list)})")
+        pack_scene_data(root, scene, output_dir)
+
+if __name__ == "__main__":
+    root = r"H:\AI\Datasets\nbv_rec_part2"
+    output_dir = r"H:\AI\Datasets\scene_info_part2"
+    scene_list = os.listdir(root)
+    from_idx = 0
+    to_idx = len(scene_list)
+    print(f"正在打包场景 {scene_list[from_idx:to_idx]}")
+
+    pack_all_scenes(root, scene_list[from_idx:to_idx], output_dir)
+    print("打包完成")
+

preprocess/pack_upload_data.py

@@ -0,0 +1,41 @@
+import os
+import shutil
+
+def pack_scene_data(root, scene, output_dir):
+    scene_dir = os.path.join(output_dir, scene)
+    if not os.path.exists(scene_dir):
+        os.makedirs(scene_dir)
+
+    pts_dir = os.path.join(root, scene, "pts")
+    if os.path.exists(pts_dir):
+        shutil.move(pts_dir, os.path.join(scene_dir, "pts"))
+
+    camera_dir = os.path.join(root, scene, "camera_params")
+    if os.path.exists(camera_dir):
+        shutil.move(camera_dir, os.path.join(scene_dir, "camera_params"))
+
+    scene_info_file = os.path.join(root, scene, "scene_info.json")
+    if os.path.exists(scene_info_file):
+        shutil.move(scene_info_file, os.path.join(scene_dir, "scene_info.json"))
+
+    label_dir = os.path.join(root, scene, "label")
+    if os.path.exists(label_dir):
+        shutil.move(label_dir, os.path.join(scene_dir, "label"))
+
+
+def pack_all_scenes(root, scene_list, output_dir):
+    for idx, scene in enumerate(scene_list):
+        print(f"packing {scene} ({idx+1}/{len(scene_list)})")
+        pack_scene_data(root, scene, output_dir)
+
+if __name__ == "__main__":
+    root = r"H:\AI\Datasets\nbv_rec_part2"
+    output_dir = r"H:\AI\Datasets\upload_part2"
+    scene_list = os.listdir(root)
+    from_idx = 0
+    to_idx = len(scene_list)
+    print(f"packing {scene_list[from_idx:to_idx]}")
+
+    pack_all_scenes(root, scene_list[from_idx:to_idx], output_dir)
+    print("packing done")
+

preprocess/preprocessor.py

@@ -0,0 +1,185 @@
+import os
+import numpy as np
+import time
+import sys
+np.random.seed(0)
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+from utils.reconstruction import ReconstructionUtil
+from utils.data_load import DataLoadUtil
+from utils.pts import PtsUtil
+
+def save_np_pts(path, pts: np.ndarray, file_type="txt"):
+    if file_type == "txt":
+        np.savetxt(path, pts)
+    else:
+        np.save(path, pts)
+
+
+def save_target_points(root, scene, frame_idx, target_points: np.ndarray, file_type="txt"):
+    pts_path = os.path.join(root,scene, "pts", f"{frame_idx}.{file_type}")
+    if not os.path.exists(os.path.join(root,scene, "pts")):
+        os.makedirs(os.path.join(root,scene, "pts"))
+    save_np_pts(pts_path, target_points, file_type)
+
+def save_target_normals(root, scene, frame_idx, target_normals: np.ndarray, file_type="txt"):
+    pts_path = os.path.join(root,scene, "nrm", f"{frame_idx}.{file_type}")
+    if not os.path.exists(os.path.join(root,scene, "nrm")):
+        os.makedirs(os.path.join(root,scene, "nrm"))
+    save_np_pts(pts_path, target_normals, file_type)
+    
+def save_scan_points_indices(root, scene, frame_idx, scan_points_indices: np.ndarray, file_type="txt"):
+    indices_path = os.path.join(root,scene, "scan_points_indices", f"{frame_idx}.{file_type}")
+    if not os.path.exists(os.path.join(root,scene, "scan_points_indices")):
+        os.makedirs(os.path.join(root,scene, "scan_points_indices"))
+    save_np_pts(indices_path, scan_points_indices, file_type)
+
+def save_scan_points(root, scene, scan_points: np.ndarray):
+    scan_points_path = os.path.join(root,scene, "scan_points.txt")
+    save_np_pts(scan_points_path, scan_points)
+
+def get_world_points(depth, mask, cam_intrinsic, cam_extrinsic, random_downsample_N):
+    z = depth[mask]
+    i, j = np.nonzero(mask)
+    x = (j - cam_intrinsic[0, 2]) * z / cam_intrinsic[0, 0]
+    y = (i - cam_intrinsic[1, 2]) * z / cam_intrinsic[1, 1]
+
+    points_camera = np.stack((x, y, z), axis=-1).reshape(-1, 3)
+    sampled_target_points = PtsUtil.random_downsample_point_cloud(
+                points_camera, random_downsample_N
+            )
+    points_camera_aug = np.concatenate((sampled_target_points, np.ones((sampled_target_points.shape[0], 1))), axis=-1)
+    points_camera_world = np.dot(cam_extrinsic, points_camera_aug.T).T[:, :3]
+    
+    return points_camera_world
+
+def get_world_points_and_normal(depth, mask, normal, cam_intrinsic, cam_extrinsic, random_downsample_N):
+    z = depth[mask]
+    i, j = np.nonzero(mask)
+    x = (j - cam_intrinsic[0, 2]) * z / cam_intrinsic[0, 0]
+    y = (i - cam_intrinsic[1, 2]) * z / cam_intrinsic[1, 1]
+
+    points_camera = np.stack((x, y, z), axis=-1).reshape(-1, 3)
+    normal_camera = normal[mask].reshape(-1, 3)
+    sampled_target_points, idx = PtsUtil.random_downsample_point_cloud(
+                points_camera, random_downsample_N, require_idx=True
+            )
+    if len(sampled_target_points) == 0:
+        return np.zeros((0, 3)), np.zeros((0, 3))
+    sampled_normal_camera  = normal_camera[idx]
+
+    points_camera_aug = np.concatenate((sampled_target_points, np.ones((sampled_target_points.shape[0], 1))), axis=-1)
+    points_camera_world = np.dot(cam_extrinsic, points_camera_aug.T).T[:, :3]
+    
+    return points_camera_world, sampled_normal_camera
+
+def get_scan_points_indices(scan_points, mask, display_table_mask_label, cam_intrinsic, cam_extrinsic):
+    scan_points_homogeneous = np.hstack((scan_points, np.ones((scan_points.shape[0], 1))))
+    points_camera = np.dot(np.linalg.inv(cam_extrinsic), scan_points_homogeneous.T).T[:, :3]
+    points_image_homogeneous = np.dot(cam_intrinsic, points_camera.T).T
+    points_image_homogeneous /= points_image_homogeneous[:, 2:]
+    pixel_x = points_image_homogeneous[:, 0].astype(int)
+    pixel_y = points_image_homogeneous[:, 1].astype(int)
+    h, w = mask.shape[:2]
+    valid_indices = (pixel_x >= 0) & (pixel_x < w) & (pixel_y >= 0) & (pixel_y < h)
+    mask_colors = mask[pixel_y[valid_indices], pixel_x[valid_indices]]
+    selected_points_indices = np.where((mask_colors == display_table_mask_label).all(axis=-1))[0]
+    selected_points_indices = np.where(valid_indices)[0][selected_points_indices]
+    return selected_points_indices
+    
+
+def save_scene_data(root, scene, scene_idx=0, scene_total=1,file_type="txt"):
+    
+    ''' configuration '''
+    target_mask_label = (0, 255, 0)
+    display_table_mask_label=(0, 0, 255)
+    random_downsample_N = 32768
+    voxel_size=0.003
+    filter_degree = 75
+    min_z = 0.2
+    max_z = 0.5
+    
+    ''' scan points '''
+    display_table_info = DataLoadUtil.get_display_table_info(root, scene)
+    radius = display_table_info["radius"]
+    
+    scan_points = np.asarray(ReconstructionUtil.generate_scan_points(display_table_top=0,display_table_radius=radius))
+    
+    ''' read frame data(depth|mask|normal) '''
+    frame_num = DataLoadUtil.get_scene_seq_length(root, scene)
+    for frame_id in range(frame_num):
+        print(f"[scene({scene_idx}/{scene_total})|frame({frame_id}/{frame_num})]Processing {scene} frame {frame_id}")
+        path = DataLoadUtil.get_path(root, scene, frame_id)
+        cam_info = DataLoadUtil.load_cam_info(path, binocular=True)
+        depth_L, depth_R = DataLoadUtil.load_depth(
+                path, cam_info["near_plane"], 
+                cam_info["far_plane"], 
+                binocular=True
+            )
+        mask_L, mask_R = DataLoadUtil.load_seg(path, binocular=True)
+        normal_L = DataLoadUtil.load_normal(path, binocular=True, left_only=True)
+        ''' target points '''
+        mask_img_L = mask_L
+        mask_img_R = mask_R
+
+        target_mask_img_L = (mask_L == target_mask_label).all(axis=-1)
+        target_mask_img_R = (mask_R == target_mask_label).all(axis=-1)
+
+        
+        sampled_target_points_L, sampled_target_normal_L = get_world_points_and_normal(depth_L,target_mask_img_L,normal_L, cam_info["cam_intrinsic"], cam_info["cam_to_world"], random_downsample_N)
+        sampled_target_points_R = get_world_points(depth_R, target_mask_img_R, cam_info["cam_intrinsic"], cam_info["cam_to_world_R"], random_downsample_N)
+
+
+        has_points = sampled_target_points_L.shape[0] > 0 and sampled_target_points_R.shape[0] > 0
+        if has_points:
+            target_points, overlap_idx = PtsUtil.get_overlapping_points(
+                    sampled_target_points_L, sampled_target_points_R, voxel_size, require_idx=True
+                )
+            sampled_target_normal_L = sampled_target_normal_L[overlap_idx]
+
+        if has_points:
+            has_points = target_points.shape[0] > 0
+
+        if has_points:
+            target_points, target_normals = PtsUtil.filter_points(
+                target_points, sampled_target_normal_L, cam_info["cam_to_world"], theta_limit = filter_degree, z_range=(min_z, max_z)
+                )
+
+
+        ''' scan points indices '''
+        scan_points_indices_L = get_scan_points_indices(scan_points, mask_img_L, display_table_mask_label, cam_info["cam_intrinsic"], cam_info["cam_to_world"]) 
+        scan_points_indices_R = get_scan_points_indices(scan_points, mask_img_R, display_table_mask_label, cam_info["cam_intrinsic"], cam_info["cam_to_world_R"])
+        scan_points_indices = np.intersect1d(scan_points_indices_L, scan_points_indices_R)
+        
+        if not has_points:
+            target_points = np.zeros((0, 3))
+            target_normals = np.zeros((0, 3))
+            
+        save_target_points(root, scene, frame_id, target_points, file_type=file_type)
+        save_target_normals(root, scene, frame_id, target_normals, file_type=file_type)
+        save_scan_points_indices(root, scene, frame_id, scan_points_indices, file_type=file_type)
+    
+    save_scan_points(root, scene, scan_points) # The "done" flag of scene preprocess
+
+
+if __name__ == "__main__":
+    #root = "/media/hofee/repository/new_data_with_normal"
+    root = r"/media/hofee/data/data/test_bottle/view"
+    scene_list = os.listdir(root)
+    from_idx = 0 # 1000
+    to_idx = len(scene_list) # 1500
+
+
+    cnt = 0
+    import time
+    total = to_idx - from_idx
+    for scene in scene_list[from_idx:to_idx]:
+        start = time.time()
+        if os.path.exists(os.path.join(root, scene, "scan_points.txt")):
+            print(f"Scene {scene} has been processed")
+            cnt+=1
+            continue
+        save_scene_data(root, scene, cnt, total, file_type="npy")
+        cnt+=1
+        end = time.time()
+        print(f"Time cost: {end-start}")

runners/data_spliter.py

@@ -0,0 +1,57 @@
+import os
+import random
+from PytorchBoot.runners.runner import Runner
+from PytorchBoot.config import ConfigManager
+from PytorchBoot.utils import Log
+import PytorchBoot.stereotype as stereotype
+from PytorchBoot.status import status_manager
+
+@stereotype.runner("data_spliter")
+class DataSpliter(Runner):
+    def __init__(self, config):
+        super().__init__(config)
+        self.load_experiment("data_split")
+        self.root_dir = ConfigManager.get("runner", "split", "root_dir")
+        self.type = ConfigManager.get("runner", "split", "type")
+        self.datasets = ConfigManager.get("runner", "split", "datasets")
+        self.datapath_list = self.load_all_datapath()
+    
+    def run(self):
+        self.split_dataset()
+        
+    def split_dataset(self):
+        
+        random.shuffle(self.datapath_list)
+        start_idx = 0
+        for dataset_idx in range(len(self.datasets)):
+            dataset = list(self.datasets.keys())[dataset_idx]
+            ratio = self.datasets[dataset]["ratio"]
+            path = self.datasets[dataset]["path"]
+            split_size = int(len(self.datapath_list) * ratio)
+            split_files = self.datapath_list[start_idx:start_idx + split_size]
+            start_idx += split_size
+            self.save_split_files(path, split_files)
+            status_manager.set_progress("split", "data_splitor", "split dataset", dataset_idx, len(self.datasets))
+            Log.success(f"save {dataset} split files to {path}")
+        status_manager.set_progress("split", "data_splitor", "split dataset", len(self.datasets), len(self.datasets))
+    def save_split_files(self, path, split_files):
+        os.makedirs(os.path.dirname(path), exist_ok=True)
+        with open(path, "w") as f:
+            f.write("\n".join(split_files))
+        
+        
+    def load_all_datapath(self):
+        return os.listdir(self.root_dir)
+    
+    def create_experiment(self, backup_name=None):
+        super().create_experiment(backup_name)
+    
+    def load_experiment(self, backup_name=None):
+        super().load_experiment(backup_name)
+    
+    
+
+
+    
+        
+    

runners/evaluate_uncertainty_guide.py

@@ -0,0 +1,360 @@
+import os
+import json
+from utils.render import RenderUtil
+from utils.pose import PoseUtil
+from utils.pts import PtsUtil
+from utils.reconstruction import ReconstructionUtil
+from beans.predict_result import PredictResult
+
+import torch
+from tqdm import tqdm
+import numpy as np
+import pickle
+
+from PytorchBoot.config import ConfigManager
+import PytorchBoot.namespace as namespace
+import PytorchBoot.stereotype as stereotype
+from PytorchBoot.factory import ComponentFactory
+
+from PytorchBoot.dataset import BaseDataset
+from PytorchBoot.runners.runner import Runner
+from PytorchBoot.utils import Log
+from PytorchBoot.status import status_manager
+from utils.data_load import DataLoadUtil
+
+@stereotype.runner("evaluate_uncertainty_guide")
+class EvaluateUncertaintyGuide(Runner):
+    def __init__(self, config_path):
+        
+        super().__init__(config_path)
+    
+        self.script_path = ConfigManager.get(namespace.Stereotype.RUNNER, "blender_script_path")
+        self.output_dir = ConfigManager.get(namespace.Stereotype.RUNNER, "output_dir")
+        self.voxel_size = ConfigManager.get(namespace.Stereotype.RUNNER, "voxel_size")
+        self.min_new_area = ConfigManager.get(namespace.Stereotype.RUNNER, "min_new_area")
+        CM = 0.01
+        self.min_new_pts_num = self.min_new_area * (CM / self.voxel_size) ** 2
+
+        
+        self.output_data_root =  ConfigManager.get(namespace.Stereotype.RUNNER, "output_data_root")
+        self.output_data = dict()
+        for scene_name in os.listdir(self.output_data_root):
+            real_scene_name = scene_name[:-len("_poses.npy")]
+            self.output_data[real_scene_name] = np.load(os.path.join(self.output_data_root, scene_name))
+            #mport ipdb; ipdb.set_trace()
+        ''' Experiment '''
+        self.load_experiment("nbv_evaluator")
+        self.stat_result_path = os.path.join(self.output_dir, "stat.json")
+        if os.path.exists(self.stat_result_path):
+            with open(self.stat_result_path, "r") as f:
+                self.stat_result = json.load(f)
+        else:
+            self.stat_result = {}
+
+        ''' Test '''
+        self.test_config = ConfigManager.get(namespace.Stereotype.RUNNER, namespace.Mode.TEST)
+        self.test_dataset_name_list = self.test_config["dataset_list"]
+        self.test_set_list = []
+        self.test_writer_list = []
+        seen_name = set()
+        for test_dataset_name in self.test_dataset_name_list:
+            if test_dataset_name not in seen_name:
+                seen_name.add(test_dataset_name)
+            else:
+                raise ValueError("Duplicate test dataset name: {}".format(test_dataset_name))
+            test_set: BaseDataset = ComponentFactory.create(namespace.Stereotype.DATASET, test_dataset_name)
+            self.test_set_list.append(test_set)
+        self.print_info()
+        
+
+    def run(self):
+        Log.info("Loading from epoch {}.".format(self.current_epoch))
+        self.inference()
+        Log.success("Inference finished.")
+        
+
+    def inference(self):
+        #self.pipeline.eval()
+        with torch.no_grad():
+            test_set: BaseDataset
+            for dataset_idx, test_set in enumerate(self.test_set_list):
+                status_manager.set_progress("inference", "inferencer", f"dataset", dataset_idx, len(self.test_set_list))
+                test_set_name = test_set.get_name()
+
+                total=int(len(test_set))
+                for i in tqdm(range(total), desc=f"Processing {test_set_name}", ncols=100):
+                    try:
+                        data = test_set.__getitem__(i)
+                        scene_name = data["scene_name"]
+                        inference_result_path = os.path.join(self.output_dir, test_set_name, f"{scene_name}.pkl")
+                        
+                        if os.path.exists(inference_result_path):
+                            Log.info(f"Inference result already exists for scene: {scene_name}")
+                            continue
+                        
+                        status_manager.set_progress("inference", "inferencer", f"Batch[{test_set_name}]", i+1, total)
+                        output = self.predict_sequence(data)
+                        self.save_inference_result(test_set_name, data["scene_name"], output)
+                    except Exception as e:
+                        print(e)
+                        Log.error(f"Error, {e}")
+                        continue
+                    
+            status_manager.set_progress("inference", "inferencer", f"dataset", len(self.test_set_list), len(self.test_set_list))
+
+    def get_output_data(self, scene_name, idx):
+        pose_matrix = self.output_data[scene_name][idx]
+        pose_6d = PoseUtil.matrix_to_rotation_6d_numpy(pose_matrix[:3,:3])
+        pose_9d = np.concatenate([pose_6d, pose_matrix[:3,3]], axis=0).reshape(1,9)
+        import ipdb; ipdb.set_trace()
+        return {"pred_pose_9d": pose_9d}
+        
+    def predict_sequence(self, data, cr_increase_threshold=0, overlap_area_threshold=25, scan_points_threshold=10, max_iter=50, max_retry = 10, max_success=3):
+        scene_name = data["scene_name"]
+        Log.info(f"Processing scene: {scene_name}")
+        status_manager.set_status("inference", "inferencer", "scene", scene_name)
+
+        ''' data for rendering '''
+        scene_path = data["scene_path"]
+        O_to_L_pose = data["O_to_L_pose"]
+        voxel_threshold = self.voxel_size
+        filter_degree = 75
+        down_sampled_model_pts = data["gt_pts"]
+        
+        first_frame_to_world_9d = data["first_scanned_n_to_world_pose_9d"][0]
+        first_frame_to_world = np.eye(4)
+        first_frame_to_world[:3,:3] = PoseUtil.rotation_6d_to_matrix_numpy(first_frame_to_world_9d[:6])
+        first_frame_to_world[:3,3] = first_frame_to_world_9d[6:]
+        
+        ''' data for inference '''
+        input_data = {}
+        
+        input_data["combined_scanned_pts"] = torch.tensor(data["first_scanned_pts"][0], dtype=torch.float32).to(self.device).unsqueeze(0)
+        input_data["scanned_pts"] = [torch.tensor(data["first_scanned_pts"][0], dtype=torch.float32).to(self.device).unsqueeze(0)]
+        input_data["scanned_pts_mask"] = [torch.zeros(input_data["combined_scanned_pts"].shape[1], dtype=torch.bool).to(self.device).unsqueeze(0)]
+        input_data["scanned_n_to_world_pose_9d"] = [torch.tensor(data["first_scanned_n_to_world_pose_9d"], dtype=torch.float32).to(self.device)]
+        input_data["mode"] = namespace.Mode.TEST
+        input_pts_N = input_data["combined_scanned_pts"].shape[1]
+        root = os.path.dirname(scene_path)
+        display_table_info = DataLoadUtil.get_display_table_info(root, scene_name)
+        radius = display_table_info["radius"]
+        scan_points = np.asarray(ReconstructionUtil.generate_scan_points(display_table_top=0,display_table_radius=radius))
+
+        first_frame_target_pts, first_frame_target_normals, first_frame_scan_points_indices = RenderUtil.render_pts(first_frame_to_world, scene_path, self.script_path, scan_points, voxel_threshold=voxel_threshold, filter_degree=filter_degree, nO_to_nL_pose=O_to_L_pose)
+        scanned_view_pts = [first_frame_target_pts]
+        history_indices = [first_frame_scan_points_indices]
+        last_pred_cr, added_pts_num = self.compute_coverage_rate(scanned_view_pts, None, down_sampled_model_pts, threshold=voxel_threshold)
+        retry_duplication_pose = []
+        retry_no_pts_pose = []
+        retry_overlap_pose = []
+        retry = 0
+        pred_cr_seq = [last_pred_cr]
+        success = 0
+        last_pts_num = PtsUtil.voxel_downsample_point_cloud(data["first_scanned_pts"][0], voxel_threshold).shape[0]
+        #import time
+        for i in range(len(self.output_data[scene_name])):
+            #import ipdb; ipdb.set_trace()
+            Log.green(f"iter: {len(pred_cr_seq)}, retry: {retry}/{max_retry}, success: {success}/{max_success}")
+            combined_scanned_pts = np.vstack(scanned_view_pts)
+            voxel_downsampled_combined_scanned_pts_np, inverse =  self.voxel_downsample_with_mapping(combined_scanned_pts, voxel_threshold)
+            
+            output = self.get_output_data(scene_name, i)
+            pred_pose_9d = output["pred_pose_9d"]
+            import ipdb; ipdb.set_trace()
+            #pred_pose = torch.eye(4, device=pred_pose_9d.device)
+            # # save pred_pose_9d ------
+            # root = "/media/hofee/data/project/python/nbv_reconstruction/nbv_reconstruction/temp_output_result"
+            # scene_dir = os.path.join(root, scene_name)
+            # if not os.path.exists(scene_dir):
+            #     os.makedirs(scene_dir)
+            # pred_9d_path = os.path.join(scene_dir,f"pred_pose_9d_{len(pred_cr_seq)}.npy")
+            # pts_path = os.path.join(scene_dir,f"combined_scanned_pts_{len(pred_cr_seq)}.txt")
+            # np_combined_scanned_pts = input_data["combined_scanned_pts"][0].cpu().numpy()
+            # np.save(pred_9d_path, pred_pose_9d.cpu().numpy())
+            # np.savetxt(pts_path, np_combined_scanned_pts)
+            # # ----- ----- -----
+            predict_result = PredictResult(pred_pose_9d, input_pts=input_data["combined_scanned_pts"][0].cpu().numpy(), cluster_params=dict(eps=0.25, min_samples=3))
+            # -----------------------
+            # import ipdb; ipdb.set_trace()
+            # predict_result.visualize()
+            # -----------------------
+            pred_pose_9d_candidates = predict_result.candidate_9d_poses
+            for pred_pose_9d in pred_pose_9d_candidates:
+                #import ipdb; ipdb.set_trace()
+                pred_pose_9d = torch.tensor(pred_pose_9d, dtype=torch.float32).to(self.device).unsqueeze(0)
+                pred_pose[:3,:3] = PoseUtil.rotation_6d_to_matrix_tensor_batch(pred_pose_9d[:,:6])[0]
+                pred_pose[:3,3] = pred_pose_9d[0,6:]
+                try:
+                    new_target_pts, new_target_normals, new_scan_points_indices = RenderUtil.render_pts(pred_pose, scene_path, self.script_path, scan_points, voxel_threshold=voxel_threshold, filter_degree=filter_degree, nO_to_nL_pose=O_to_L_pose)
+                    #import ipdb; ipdb.set_trace()
+                    if not ReconstructionUtil.check_scan_points_overlap(history_indices, new_scan_points_indices, scan_points_threshold):
+                        curr_overlap_area_threshold = overlap_area_threshold
+                    else:
+                        curr_overlap_area_threshold = overlap_area_threshold * 0.5  
+
+                    downsampled_new_target_pts = PtsUtil.voxel_downsample_point_cloud(new_target_pts, voxel_threshold)
+                    overlap, _ = ReconstructionUtil.check_overlap(downsampled_new_target_pts, voxel_downsampled_combined_scanned_pts_np, overlap_area_threshold = curr_overlap_area_threshold, voxel_size=voxel_threshold, require_new_added_pts_num = True)
+                    if not overlap:
+                        Log.yellow("no overlap!")
+                        retry += 1
+                        retry_overlap_pose.append(pred_pose.cpu().numpy().tolist())
+                        continue
+                    
+                    history_indices.append(new_scan_points_indices)
+                except Exception as e:
+                    Log.error(f"Error in scene {scene_path}, {e}")
+                    print("current pose: ", pred_pose)
+                    print("curr_pred_cr: ", last_pred_cr)
+                    retry_no_pts_pose.append(pred_pose.cpu().numpy().tolist())
+                    retry += 1
+                    continue
+                
+                if new_target_pts.shape[0] == 0:
+                    Log.red("no pts in new target")
+                    retry_no_pts_pose.append(pred_pose.cpu().numpy().tolist())
+                    retry += 1
+                    continue
+                
+                pred_cr, _ = self.compute_coverage_rate(scanned_view_pts, new_target_pts, down_sampled_model_pts, threshold=voxel_threshold)
+                Log.yellow(f"{pred_cr}, {last_pred_cr}, max: , {data['seq_max_coverage_rate']}")
+                if pred_cr >= data["seq_max_coverage_rate"] - 1e-3:
+                    print("max coverage rate reached!: ", pred_cr)
+                    
+            
+
+                pred_cr_seq.append(pred_cr)
+                scanned_view_pts.append(new_target_pts)
+                
+                input_data["scanned_n_to_world_pose_9d"] = [torch.cat([input_data["scanned_n_to_world_pose_9d"][0], pred_pose_9d], dim=0)]
+                
+                combined_scanned_pts = np.vstack(scanned_view_pts)
+                voxel_downsampled_combined_scanned_pts_np = PtsUtil.voxel_downsample_point_cloud(combined_scanned_pts, voxel_threshold)
+                random_downsampled_combined_scanned_pts_np = PtsUtil.random_downsample_point_cloud(voxel_downsampled_combined_scanned_pts_np, input_pts_N)
+                input_data["combined_scanned_pts"] = torch.tensor(random_downsampled_combined_scanned_pts_np, dtype=torch.float32).unsqueeze(0).to(self.device)
+                input_data["scanned_pts"] = [torch.cat([input_data["scanned_pts"][0], torch.tensor(random_downsampled_combined_scanned_pts_np, dtype=torch.float32).unsqueeze(0).to(self.device)], dim=0)]
+                
+                last_pred_cr = pred_cr
+                pts_num = voxel_downsampled_combined_scanned_pts_np.shape[0]
+                Log.info(f"delta pts num:,{pts_num - last_pts_num },{pts_num}, {last_pts_num}")
+
+                if pts_num - last_pts_num < self.min_new_pts_num and pred_cr <= data["seq_max_coverage_rate"] - 1e-2:
+                    retry += 1
+                    retry_duplication_pose.append(pred_pose.cpu().numpy().tolist())
+                    Log.red(f"delta pts num < {self.min_new_pts_num}:, {pts_num}, {last_pts_num}")
+                elif pts_num - last_pts_num < self.min_new_pts_num and pred_cr > data["seq_max_coverage_rate"] - 1e-2:
+                    success += 1
+                    Log.success(f"delta pts num < {self.min_new_pts_num}:, {pts_num}, {last_pts_num}")
+
+                last_pts_num = pts_num
+            
+
+        input_data["scanned_n_to_world_pose_9d"] = input_data["scanned_n_to_world_pose_9d"][0].cpu().numpy().tolist()
+        result = {
+            "pred_pose_9d_seq": input_data["scanned_n_to_world_pose_9d"],
+            "combined_scanned_pts": input_data["combined_scanned_pts"],
+            "target_pts_seq": scanned_view_pts,
+            "coverage_rate_seq": pred_cr_seq,
+            "max_coverage_rate": data["seq_max_coverage_rate"],
+            "pred_max_coverage_rate": max(pred_cr_seq),
+            "scene_name": scene_name,
+            "retry_no_pts_pose": retry_no_pts_pose,
+            "retry_duplication_pose": retry_duplication_pose,
+            "retry_overlap_pose": retry_overlap_pose,
+            "best_seq_len": data["best_seq_len"],
+        }
+        self.stat_result[scene_name] = {
+            "coverage_rate_seq": pred_cr_seq,
+            "pred_max_coverage_rate": max(pred_cr_seq),
+            "pred_seq_len": len(pred_cr_seq),
+        }
+        print('success rate: ', max(pred_cr_seq))
+
+        return result
+
+    def voxel_downsample_with_mapping(self, point_cloud, voxel_size=0.003):
+        voxel_indices = np.floor(point_cloud / voxel_size).astype(np.int32)
+        unique_voxels, inverse, counts = np.unique(voxel_indices, axis=0, return_inverse=True, return_counts=True)
+        idx_sort = np.argsort(inverse)
+        idx_unique = idx_sort[np.cumsum(counts)-counts]
+        downsampled_points = point_cloud[idx_unique]
+        return downsampled_points, inverse
+    
+    def compute_coverage_rate(self, scanned_view_pts, new_pts, model_pts, threshold=0.005):
+        if new_pts is not None:
+            new_scanned_view_pts = scanned_view_pts + [new_pts]
+        else:
+            new_scanned_view_pts = scanned_view_pts
+        combined_point_cloud = np.vstack(new_scanned_view_pts)
+        down_sampled_combined_point_cloud = PtsUtil.voxel_downsample_point_cloud(combined_point_cloud,threshold)
+        return ReconstructionUtil.compute_coverage_rate(model_pts, down_sampled_combined_point_cloud, threshold)
+    
+    def voxel_downsample_with_mapping(self, point_cloud, voxel_size=0.003):
+        voxel_indices = np.floor(point_cloud / voxel_size).astype(np.int32)
+        unique_voxels, inverse, counts = np.unique(voxel_indices, axis=0, return_inverse=True, return_counts=True)
+        idx_sort = np.argsort(inverse)
+        idx_unique = idx_sort[np.cumsum(counts)-counts]
+        downsampled_points = point_cloud[idx_unique]
+        return downsampled_points, inverse
+
+    def save_inference_result(self, dataset_name, scene_name, output):
+        dataset_dir = os.path.join(self.output_dir, dataset_name)
+        if not os.path.exists(dataset_dir):
+            os.makedirs(dataset_dir)
+        output_path = os.path.join(dataset_dir, f"{scene_name}.pkl")
+        pickle.dump(output, open(output_path, "wb"))
+        with open(self.stat_result_path, "w") as f:
+            json.dump(self.stat_result, f)
+    
+
+    def get_checkpoint_path(self, is_last=False):
+        return os.path.join(self.experiment_path, namespace.Direcotry.CHECKPOINT_DIR_NAME,
+                            "Epoch_{}.pth".format(
+                                self.current_epoch if self.current_epoch != -1 and not is_last else "last"))
+
+    def load_checkpoint(self, is_last=False):
+        self.load(self.get_checkpoint_path(is_last))
+        Log.success(f"Loaded checkpoint from {self.get_checkpoint_path(is_last)}")
+        if is_last:
+            checkpoint_root = os.path.join(self.experiment_path, namespace.Direcotry.CHECKPOINT_DIR_NAME)
+            meta_path = os.path.join(checkpoint_root, "meta.json")
+            if not os.path.exists(meta_path):
+                raise FileNotFoundError(
+                    "No checkpoint meta.json file in the experiment {}".format(self.experiments_config["name"]))
+            file_path = os.path.join(checkpoint_root, "meta.json")
+            with open(file_path, "r") as f:
+                meta = json.load(f)
+            self.current_epoch = meta["last_epoch"]
+            self.current_iter = meta["last_iter"]
+
+    def load_experiment(self, backup_name=None):
+        super().load_experiment(backup_name)
+        self.current_epoch = self.experiments_config["epoch"]
+        #self.load_checkpoint(is_last=(self.current_epoch == -1))
+
+    def create_experiment(self, backup_name=None):
+        super().create_experiment(backup_name)
+       
+        
+    def load(self, path):
+        state_dict = torch.load(path)
+        self.pipeline.load_state_dict(state_dict)
+
+    def print_info(self):
+        def print_dataset(dataset: BaseDataset):
+            config = dataset.get_config()
+            name = dataset.get_name()
+            Log.blue(f"Dataset: {name}")
+            for k,v in config.items():
+                Log.blue(f"\t{k}: {v}")
+
+        super().print_info()
+        table_size = 70
+        Log.blue(f"{'+' + '-' * (table_size // 2)} Pipeline {'-' * (table_size // 2)}" + '+')
+        #Log.blue(self.pipeline)
+        Log.blue(f"{'+' + '-' * (table_size // 2)} Datasets {'-' * (table_size // 2)}" + '+')
+        for i, test_set in enumerate(self.test_set_list):
+            Log.blue(f"test dataset {i}: ")
+            print_dataset(test_set)
+
+        Log.blue(f"{'+' + '-' * (table_size // 2)}----------{'-' * (table_size // 2)}" + '+')
+