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add KNN interpolation for pointcloud

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0nhc 2024-11-03 01:28:57 -05:00
parent ed5915fa40
commit 28bfe0e552
2 changed files with 691 additions and 668 deletions
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1329
gsnet_input_points.txt
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30
src/active_grasp/active_perception_policy.py
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@ -15,6 +15,7 @@ import time
from visualization_msgs.msg import Marker, MarkerArray from visualization_msgs.msg import Marker, MarkerArray
from geometry_msgs.msg import Pose from geometry_msgs.msg import Pose
import tf import tf
from sklearn.neighbors import NearestNeighbors
import sensor_msgs.point_cloud2 as pc2 import sensor_msgs.point_cloud2 as pc2
from sensor_msgs.msg import PointCloud2, PointField from sensor_msgs.msg import PointCloud2, PointField
@ -493,14 +494,21 @@ class ActivePerceptionSingleViewPolicy(SingleViewPolicy):
scene_points = np.asarray(scene_points) scene_points = np.asarray(scene_points)
if scene_sample_num > 0: if scene_sample_num > 0:
if scene_points.shape[0] < scene_sample_num: if scene_points.shape[0] < scene_sample_num:
scene_sample_num = scene_points.shape[0]
print("Scene points are less than the required sample number") print("Scene points are less than the required sample number")
scene_points = scene_points[np.random.choice(scene_points.shape[0], scene_sample_num, replace=False)] num_interpolation = scene_sample_num - scene_points.shape[0]
scene_points = self.interpolate_point_cloud(scene_points, num_interpolation)
print("Interpolated scene points. Shape: "+str(scene_points.shape))
else:
scene_points = scene_points[np.random.choice(scene_points.shape[0], scene_sample_num, replace=False)]
if target_sample_num > 0: if target_sample_num > 0:
if target_points.shape[0] < target_sample_num: if target_points.shape[0] < target_sample_num:
target_sample_num = target_points.shape[0]
print("Target points are less than the required sample number") print("Target points are less than the required sample number")
target_points = target_points[np.random.choice(target_points.shape[0], target_sample_num, replace=False)] num_interpolation = target_sample_num - target_points.shape[0]
target_points = self.interpolate_point_cloud(target_points, num_interpolation)
print("Interpolated target points. Shape: "+str(target_points.shape))
else:
target_points = target_points[np.random.choice(target_points.shape[0], target_sample_num, replace=False)]
# reshape points # reshape points
target_points = target_points.reshape(1, target_points.shape[0], 3) target_points = target_points.reshape(1, target_points.shape[0], 3)
@ -511,3 +519,17 @@ class ActivePerceptionSingleViewPolicy(SingleViewPolicy):
scene_points = torch.from_numpy(scene_points).float().to("cuda:0") scene_points = torch.from_numpy(scene_points).float().to("cuda:0")
return target_points, scene_points return target_points, scene_points
def interpolate_point_cloud(self, points, num_new_points):
# Fit NearestNeighbors on existing points
nbrs = NearestNeighbors(n_neighbors=5).fit(points)
interpolated_points = []
for _ in range(num_new_points):
random_point = points[np.random.choice(len(points))]
distances, indices = nbrs.kneighbors([random_point])
neighbors = points[indices[0]]
new_point = neighbors.mean(axis=0) # Average of neighbors
interpolated_points.append(new_point)
return np.vstack([points, interpolated_points])