KD Tree#
kd_tree_feature_matching.py#
1# ----------------------------------------------------------------------------
2# - Open3D: www.open3d.org -
3# ----------------------------------------------------------------------------
4# Copyright (c) 2018-2023 www.open3d.org
5# SPDX-License-Identifier: MIT
6# ----------------------------------------------------------------------------
7
8import numpy as np
9import open3d as o3d
10
11if __name__ == "__main__":
12
13 print("Load two aligned point clouds.")
14 demo_data = o3d.data.DemoFeatureMatchingPointClouds()
15 pcd0 = o3d.io.read_point_cloud(demo_data.point_cloud_paths[0])
16 pcd1 = o3d.io.read_point_cloud(demo_data.point_cloud_paths[1])
17
18 pcd0.paint_uniform_color([1, 0.706, 0])
19 pcd1.paint_uniform_color([0, 0.651, 0.929])
20 o3d.visualization.draw_geometries([pcd0, pcd1])
21 print("Load their FPFH feature and evaluate.")
22 print("Black : matching distance > 0.2")
23 print("White : matching distance = 0")
24 feature0 = o3d.io.read_feature(demo_data.fpfh_feature_paths[0])
25 feature1 = o3d.io.read_feature(demo_data.fpfh_feature_paths[1])
26
27 fpfh_tree = o3d.geometry.KDTreeFlann(feature1)
28 for i in range(len(pcd0.points)):
29 [_, idx, _] = fpfh_tree.search_knn_vector_xd(feature0.data[:, i], 1)
30 dis = np.linalg.norm(pcd0.points[i] - pcd1.points[idx[0]])
31 c = (0.2 - np.fmin(dis, 0.2)) / 0.2
32 pcd0.colors[i] = [c, c, c]
33 o3d.visualization.draw_geometries([pcd0])
34 print("")
35
36 print("Load their L32D feature and evaluate.")
37 print("Black : matching distance > 0.2")
38 print("White : matching distance = 0")
39 feature0 = o3d.io.read_feature(demo_data.l32d_feature_paths[0])
40 feature1 = o3d.io.read_feature(demo_data.l32d_feature_paths[1])
41
42 fpfh_tree = o3d.geometry.KDTreeFlann(feature1)
43 for i in range(len(pcd0.points)):
44 [_, idx, _] = fpfh_tree.search_knn_vector_xd(feature0.data[:, i], 1)
45 dis = np.linalg.norm(pcd0.points[i] - pcd1.points[idx[0]])
46 c = (0.2 - np.fmin(dis, 0.2)) / 0.2
47 pcd0.colors[i] = [c, c, c]
48 o3d.visualization.draw_geometries([pcd0])
49 print("")
kd_tree_search.py#
1# ----------------------------------------------------------------------------
2# - Open3D: www.open3d.org -
3# ----------------------------------------------------------------------------
4# Copyright (c) 2018-2023 www.open3d.org
5# SPDX-License-Identifier: MIT
6# ----------------------------------------------------------------------------
7"""Build a KDTree and use it for neighbour search"""
8
9import open3d as o3d
10import numpy as np
11
12
13def radius_search():
14 print("Loading pointcloud ...")
15 sample_pcd_data = o3d.data.PCDPointCloud()
16 pcd = o3d.io.read_point_cloud(sample_pcd_data.path)
17 pcd_tree = o3d.geometry.KDTreeFlann(pcd)
18
19 print(
20 "Find the neighbors of 50000th point with distance less than 0.2, and painting them green ..."
21 )
22 [k, idx, _] = pcd_tree.search_radius_vector_3d(pcd.points[50000], 0.2)
23 np.asarray(pcd.colors)[idx[1:], :] = [0, 1, 0]
24
25 print("Displaying the final point cloud ...\n")
26 o3d.visualization.draw([pcd])
27
28
29def knn_search():
30 print("Loading pointcloud ...")
31 sample_pcd = o3d.data.PCDPointCloud()
32 pcd = o3d.io.read_point_cloud(sample_pcd.path)
33 pcd_tree = o3d.geometry.KDTreeFlann(pcd)
34
35 print(
36 "Find the 2000 nearest neighbors of 50000th point, and painting them red ..."
37 )
38 [k, idx, _] = pcd_tree.search_knn_vector_3d(pcd.points[50000], 2000)
39 np.asarray(pcd.colors)[idx[1:], :] = [1, 0, 0]
40
41 print("Displaying the final point cloud ...\n")
42 o3d.visualization.draw([pcd])
43
44
45if __name__ == "__main__":
46 knn_search()
47 radius_search()