Pipelines

colored_icp_registration.py

27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82

import open3d as o3d
import numpy as np
import copy


def draw_registration_result(source, target, transformation):
    source_temp = copy.deepcopy(source)
    source_temp.transform(transformation)
    o3d.visualization.draw([source_temp, target])


print("Load two point clouds and show initial pose ...")
ply_data = o3d.data.DemoColoredICPPointClouds()
source = o3d.io.read_point_cloud(ply_data.paths[0])
target = o3d.io.read_point_cloud(ply_data.paths[1])

if __name__ == "__main__":
    # Draw initial alignment.
    current_transformation = np.identity(4)
    # draw_registration_result(source, target, current_transformation)
    print(current_transformation)

    # Colored pointcloud registration.
    # This is implementation of following paper:
    # J. Park, Q.-Y. Zhou, V. Koltun,
    # Colored Point Cloud Registration Revisited, ICCV 2017.
    voxel_radius = [0.04, 0.02, 0.01]
    max_iter = [50, 30, 14]
    current_transformation = np.identity(4)
    print("Colored point cloud registration ...\n")
    for scale in range(3):
        iter = max_iter[scale]
        radius = voxel_radius[scale]
        print([iter, radius, scale])

        print("1. Downsample with a voxel size %.2f" % radius)
        source_down = source.voxel_down_sample(radius)
        target_down = target.voxel_down_sample(radius)

        print("2. Estimate normal")
        source_down.estimate_normals(
            o3d.geometry.KDTreeSearchParamHybrid(radius=radius * 2, max_nn=30))
        target_down.estimate_normals(
            o3d.geometry.KDTreeSearchParamHybrid(radius=radius * 2, max_nn=30))

        print("3. Applying colored point cloud registration")
        result_icp = o3d.pipelines.registration.registration_colored_icp(
            source_down, target_down, radius, current_transformation,
            o3d.pipelines.registration.TransformationEstimationForColoredICP(),
            o3d.pipelines.registration.ICPConvergenceCriteria(
                relative_fitness=1e-6, relative_rmse=1e-6, max_iteration=iter))
        current_transformation = result_icp.transformation
        print(result_icp, "\n")
    # draw_registration_result(source, target, result_icp.transformation)
    print(current_transformation)

icp_registration.py

27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80

import open3d as o3d
import numpy as np
import copy


def draw_registration_result(source, target, transformation):
    source_temp = copy.deepcopy(source)
    target_temp = copy.deepcopy(target)
    source_temp.paint_uniform_color([1, 0.706, 0])
    target_temp.paint_uniform_color([0, 0.651, 0.929])
    source_temp.transform(transformation)
    o3d.visualization.draw([source_temp, target_temp])


def point_to_point_icp(source, target, threshold, trans_init):
    print("Apply point-to-point ICP")
    reg_p2p = o3d.pipelines.registration.registration_icp(
        source, target, threshold, trans_init,
        o3d.pipelines.registration.TransformationEstimationPointToPoint())
    print(reg_p2p)
    print("Transformation is:")
    print(reg_p2p.transformation, "\n")
    draw_registration_result(source, target, reg_p2p.transformation)


def point_to_plane_icp(source, target, threshold, trans_init):
    print("Apply point-to-plane ICP")
    reg_p2l = o3d.pipelines.registration.registration_icp(
        source, target, threshold, trans_init,
        o3d.pipelines.registration.TransformationEstimationPointToPlane())
    print(reg_p2l)
    print("Transformation is:")
    print(reg_p2l.transformation, "\n")
    draw_registration_result(source, target, reg_p2l.transformation)


if __name__ == "__main__":
    pcd_data = o3d.data.DemoICPPointClouds()
    source = o3d.io.read_point_cloud(pcd_data.paths[0])
    target = o3d.io.read_point_cloud(pcd_data.paths[1])
    threshold = 0.02
    trans_init = np.asarray([[0.862, 0.011, -0.507, 0.5],
                             [-0.139, 0.967, -0.215, 0.7],
                             [0.487, 0.255, 0.835, -1.4], [0.0, 0.0, 0.0, 1.0]])
    draw_registration_result(source, target, trans_init)

    print("Initial alignment")
    evaluation = o3d.pipelines.registration.evaluate_registration(
        source, target, threshold, trans_init)
    print(evaluation, "\n")

    point_to_point_icp(source, target, threshold, trans_init)
    point_to_plane_icp(source, target, threshold, trans_init)

multiway_registration.py

 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124

import open3d as o3d
import numpy as np


def load_point_clouds(voxel_size=0.0):
    pcd_data = o3d.data.DemoICPPointClouds()
    pcds = []
    for i in range(3):
        pcd = o3d.io.read_point_cloud(pcd_data.paths[i])
        pcd_down = pcd.voxel_down_sample(voxel_size=voxel_size)
        pcds.append(pcd_down)
    return pcds


def pairwise_registration(source, target, max_correspondence_distance_coarse,
                          max_correspondence_distance_fine):
    print("Apply point-to-plane ICP")
    icp_coarse = o3d.pipelines.registration.registration_icp(
        source, target, max_correspondence_distance_coarse, np.identity(4),
        o3d.pipelines.registration.TransformationEstimationPointToPlane())
    icp_fine = o3d.pipelines.registration.registration_icp(
        source, target, max_correspondence_distance_fine,
        icp_coarse.transformation,
        o3d.pipelines.registration.TransformationEstimationPointToPlane())
    transformation_icp = icp_fine.transformation
    information_icp = o3d.pipelines.registration.get_information_matrix_from_point_clouds(
        source, target, max_correspondence_distance_fine,
        icp_fine.transformation)
    return transformation_icp, information_icp


def full_registration(pcds, max_correspondence_distance_coarse,
                      max_correspondence_distance_fine):
    pose_graph = o3d.pipelines.registration.PoseGraph()
    odometry = np.identity(4)
    pose_graph.nodes.append(o3d.pipelines.registration.PoseGraphNode(odometry))
    n_pcds = len(pcds)
    for source_id in range(n_pcds):
        for target_id in range(source_id + 1, n_pcds):
            transformation_icp, information_icp = pairwise_registration(
                pcds[source_id], pcds[target_id],
                max_correspondence_distance_coarse,
                max_correspondence_distance_fine)
            print("Build o3d.pipelines.registration.PoseGraph")
            if target_id == source_id + 1:  # odometry case
                odometry = np.dot(transformation_icp, odometry)
                pose_graph.nodes.append(
                    o3d.pipelines.registration.PoseGraphNode(
                        np.linalg.inv(odometry)))
                pose_graph.edges.append(
                    o3d.pipelines.registration.PoseGraphEdge(source_id,
                                                             target_id,
                                                             transformation_icp,
                                                             information_icp,
                                                             uncertain=False))
            else:  # loop closure case
                pose_graph.edges.append(
                    o3d.pipelines.registration.PoseGraphEdge(source_id,
                                                             target_id,
                                                             transformation_icp,
                                                             information_icp,
                                                             uncertain=True))
    return pose_graph


if __name__ == "__main__":
    voxel_size = 0.02
    pcds_down = load_point_clouds(voxel_size)
    o3d.visualization.draw(pcds_down)

    print("Full registration ...")
    max_correspondence_distance_coarse = voxel_size * 15
    max_correspondence_distance_fine = voxel_size * 1.5
    with o3d.utility.VerbosityContextManager(
            o3d.utility.VerbosityLevel.Debug) as cm:
        pose_graph = full_registration(pcds_down,
                                       max_correspondence_distance_coarse,
                                       max_correspondence_distance_fine)

    print("Optimizing PoseGraph ...")
    option = o3d.pipelines.registration.GlobalOptimizationOption(
        max_correspondence_distance=max_correspondence_distance_fine,
        edge_prune_threshold=0.25,
        reference_node=0)
    with o3d.utility.VerbosityContextManager(
            o3d.utility.VerbosityLevel.Debug) as cm:
        o3d.pipelines.registration.global_optimization(
            pose_graph,
            o3d.pipelines.registration.GlobalOptimizationLevenbergMarquardt(),
            o3d.pipelines.registration.GlobalOptimizationConvergenceCriteria(),
            option)

    print("Transform points and display")
    for point_id in range(len(pcds_down)):
        print(pose_graph.nodes[point_id].pose)
        pcds_down[point_id].transform(pose_graph.nodes[point_id].pose)
    o3d.visualization.draw(pcds_down)

pose_graph_optimization.py

27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
import open3d as o3d
import numpy as np
import os

if __name__ == "__main__":

    o3d.utility.set_verbosity_level(o3d.utility.VerbosityLevel.Debug)

    print("")
    print(
        "Parameters for o3d.pipelines.registration.PoseGraph optimization ...")
    method = o3d.pipelines.registration.GlobalOptimizationLevenbergMarquardt()
    criteria = o3d.pipelines.registration.GlobalOptimizationConvergenceCriteria(
    )
    option = o3d.pipelines.registration.GlobalOptimizationOption()
    print("")
    print(method)
    print(criteria)
    print(option)
    print("")

    print(
        "Optimizing Fragment o3d.pipelines.registration.PoseGraph using open3d ..."
    )

    pose_graph_data = o3d.data.DemoPoseGraphOptimization()
    pose_graph_fragment = o3d.io.read_pose_graph(
        pose_graph_data.pose_graph_fragment_path)
    print(pose_graph_fragment)
    o3d.pipelines.registration.global_optimization(pose_graph_fragment, method,
                                                   criteria, option)
    o3d.io.write_pose_graph(
        os.path.join('pose_graph_example_fragment_optimized.json'),
        pose_graph_fragment)
    print("")

    print(
        "Optimizing Global o3d.pipelines.registration.PoseGraph using open3d ..."
    )
    pose_graph_global = o3d.io.read_pose_graph(
        pose_graph_data.pose_graph_global_path)
    print(pose_graph_global)
    o3d.pipelines.registration.global_optimization(pose_graph_global, method,
                                                   criteria, option)
    o3d.io.write_pose_graph(
        os.path.join('pose_graph_example_global_optimized.json'),
        pose_graph_global)

registration_fgr.py

 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
import open3d as o3d

import argparse


def preprocess_point_cloud(pcd, voxel_size):
    pcd_down = pcd.voxel_down_sample(voxel_size)
    pcd_down.estimate_normals(
        o3d.geometry.KDTreeSearchParamHybrid(radius=voxel_size * 2.0,
                                             max_nn=30))
    pcd_fpfh = o3d.pipelines.registration.compute_fpfh_feature(
        pcd_down,
        o3d.geometry.KDTreeSearchParamHybrid(radius=voxel_size * 5.0,
                                             max_nn=100))
    return (pcd_down, pcd_fpfh)


if __name__ == '__main__':
    pcd_data = o3d.data.DemoICPPointClouds()
    parser = argparse.ArgumentParser(
        'Global point cloud registration example with RANSAC')
    parser.add_argument('src',
                        type=str,
                        default=pcd_data.paths[0],
                        nargs='?',
                        help='path to src point cloud')
    parser.add_argument('dst',
                        type=str,
                        default=pcd_data.paths[1],
                        nargs='?',
                        help='path to dst point cloud')
    parser.add_argument('--voxel_size',
                        type=float,
                        default=0.05,
                        help='voxel size in meter used to downsample inputs')
    parser.add_argument(
        '--distance_multiplier',
        type=float,
        default=1.5,
        help='multipler used to compute distance threshold'
        'between correspondences.'
        'Threshold is computed by voxel_size * distance_multiplier.')
    parser.add_argument('--max_iterations',
                        type=int,
                        default=64,
                        help='number of max FGR iterations')
    parser.add_argument(
        '--max_tuples',
        type=int,
        default=1000,
        help='max number of accepted tuples for correspondence filtering')

    args = parser.parse_args()

    voxel_size = args.voxel_size
    distance_threshold = args.distance_multiplier * voxel_size

    o3d.utility.set_verbosity_level(o3d.utility.VerbosityLevel.Debug)
    print('Reading inputs')
    src = o3d.io.read_point_cloud(args.src)
    dst = o3d.io.read_point_cloud(args.dst)

    print('Downsampling inputs')
    src_down, src_fpfh = preprocess_point_cloud(src, voxel_size)
    dst_down, dst_fpfh = preprocess_point_cloud(dst, voxel_size)

    print('Running FGR')
    result = o3d.pipelines.registration.registration_fgr_based_on_feature_matching(
        src_down, dst_down, src_fpfh, dst_fpfh,
        o3d.pipelines.registration.FastGlobalRegistrationOption(
            maximum_correspondence_distance=distance_threshold,
            iteration_number=args.max_iterations,
            maximum_tuple_count=args.max_tuples))

    src.paint_uniform_color([1, 0, 0])
    dst.paint_uniform_color([0, 1, 0])
    o3d.visualization.draw([src.transform(result.transformation), dst])

registration_ransac.py

 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
import open3d as o3d

import argparse


def preprocess_point_cloud(pcd, voxel_size):
    pcd_down = pcd.voxel_down_sample(voxel_size)
    pcd_down.estimate_normals(
        o3d.geometry.KDTreeSearchParamHybrid(radius=voxel_size * 2.0,
                                             max_nn=30))
    pcd_fpfh = o3d.pipelines.registration.compute_fpfh_feature(
        pcd_down,
        o3d.geometry.KDTreeSearchParamHybrid(radius=voxel_size * 5.0,
                                             max_nn=100))
    return (pcd_down, pcd_fpfh)


if __name__ == '__main__':
    pcd_data = o3d.data.DemoICPPointClouds()
    parser = argparse.ArgumentParser(
        'Global point cloud registration example with RANSAC')
    parser.add_argument('src',
                        type=str,
                        default=pcd_data.paths[0],
                        nargs='?',
                        help='path to src point cloud')
    parser.add_argument('dst',
                        type=str,
                        default=pcd_data.paths[1],
                        nargs='?',
                        help='path to dst point cloud')
    parser.add_argument('--voxel_size',
                        type=float,
                        default=0.05,
                        help='voxel size in meter used to downsample inputs')
    parser.add_argument(
        '--distance_multiplier',
        type=float,
        default=1.5,
        help='multipler used to compute distance threshold'
        'between correspondences.'
        'Threshold is computed by voxel_size * distance_multiplier.')
    parser.add_argument('--max_iterations',
                        type=int,
                        default=1000000,
                        help='number of max RANSAC iterations')
    parser.add_argument('--confidence',
                        type=float,
                        default=0.999,
                        help='RANSAC confidence')
    parser.add_argument(
        '--mutual_filter',
        action='store_true',
        help='whether to use mutual filter for putative correspondences')

    args = parser.parse_args()

    voxel_size = args.voxel_size
    distance_threshold = args.distance_multiplier * voxel_size

    o3d.utility.set_verbosity_level(o3d.utility.VerbosityLevel.Debug)
    print('Reading inputs')
    src = o3d.io.read_point_cloud(args.src)
    dst = o3d.io.read_point_cloud(args.dst)

    print('Downsampling inputs')
    src_down, src_fpfh = preprocess_point_cloud(src, voxel_size)
    dst_down, dst_fpfh = preprocess_point_cloud(dst, voxel_size)

    print('Running RANSAC')
    result = o3d.pipelines.registration.registration_ransac_based_on_feature_matching(
        src_down,
        dst_down,
        src_fpfh,
        dst_fpfh,
        mutual_filter=args.mutual_filter,
        max_correspondence_distance=distance_threshold,
        estimation_method=o3d.pipelines.registration.
        TransformationEstimationPointToPoint(False),
        ransac_n=3,
        checkers=[
            o3d.pipelines.registration.CorrespondenceCheckerBasedOnEdgeLength(
                0.9),
            o3d.pipelines.registration.CorrespondenceCheckerBasedOnDistance(
                distance_threshold)
        ],
        criteria=o3d.pipelines.registration.RANSACConvergenceCriteria(
            args.max_iterations, args.confidence))

    src.paint_uniform_color([1, 0, 0])
    dst.paint_uniform_color([0, 1, 0])
    o3d.visualization.draw([src.transform(result.transformation), dst])

rgbd_integration_uniform.py

27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
import open3d as o3d
import numpy as np

import os, sys

pyexample_path = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
sys.path.append(pyexample_path)

from open3d_example import read_trajectory

if __name__ == "__main__":
    rgbd_data = o3d.data.SampleRedwoodRGBDImages()
    camera_poses = read_trajectory(rgbd_data.odometry_log_path)
    camera_intrinsics = o3d.camera.PinholeCameraIntrinsic(
        o3d.camera.PinholeCameraIntrinsicParameters.PrimeSenseDefault)
    volume = o3d.pipelines.integration.UniformTSDFVolume(
        length=4.0,
        resolution=512,
        sdf_trunc=0.04,
        color_type=o3d.pipelines.integration.TSDFVolumeColorType.RGB8,
    )

    for i in range(len(camera_poses)):
        print("Integrate {:d}-th image into the volume.".format(i))
        color = o3d.io.read_image(rgbd_data.color_paths[i])
        depth = o3d.io.read_image(rgbd_data.depth_paths[i])

        rgbd = o3d.geometry.RGBDImage.create_from_color_and_depth(
            color, depth, depth_trunc=4.0, convert_rgb_to_intensity=False)
        volume.integrate(
            rgbd,
            camera_intrinsics,
            np.linalg.inv(camera_poses[i].pose),
        )

    print("Extract triangle mesh")
    mesh = volume.extract_triangle_mesh()
    mesh.compute_vertex_normals()
    o3d.visualization.draw_geometries([mesh])

    print("Extract voxel-aligned debugging point cloud")
    voxel_pcd = volume.extract_voxel_point_cloud()
    o3d.visualization.draw_geometries([voxel_pcd])

    print("Extract voxel-aligned debugging voxel grid")
    voxel_grid = volume.extract_voxel_grid()
    # o3d.visualization.draw_geometries([voxel_grid])

    # print("Extract point cloud")
    # pcd = volume.extract_point_cloud()
    # o3d.visualization.draw_geometries([pcd])

rgbd_odometry.py

27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76

import open3d as o3d
import numpy as np

if __name__ == "__main__":
    pinhole_camera_intrinsic = o3d.camera.PinholeCameraIntrinsic(
        o3d.camera.PinholeCameraIntrinsicParameters.PrimeSenseDefault)
    rgbd_data = o3d.data.SampleRedwoodRGBDImages()
    source_color = o3d.io.read_image(rgbd_data.color_paths[0])
    source_depth = o3d.io.read_image(rgbd_data.depth_paths[0])
    target_color = o3d.io.read_image(rgbd_data.color_paths[1])
    target_depth = o3d.io.read_image(rgbd_data.depth_paths[1])

    source_rgbd_image = o3d.geometry.RGBDImage.create_from_color_and_depth(
        source_color, source_depth)
    target_rgbd_image = o3d.geometry.RGBDImage.create_from_color_and_depth(
        target_color, target_depth)
    target_pcd = o3d.geometry.PointCloud.create_from_rgbd_image(
        target_rgbd_image, pinhole_camera_intrinsic)

    option = o3d.pipelines.odometry.OdometryOption()
    odo_init = np.identity(4)
    print(option)

    [success_color_term, trans_color_term,
     info] = o3d.pipelines.odometry.compute_rgbd_odometry(
         source_rgbd_image, target_rgbd_image,
         pinhole_camera_intrinsic, odo_init,
         o3d.pipelines.odometry.RGBDOdometryJacobianFromColorTerm(), option)
    [success_hybrid_term, trans_hybrid_term,
     info] = o3d.pipelines.odometry.compute_rgbd_odometry(
         source_rgbd_image, target_rgbd_image,
         pinhole_camera_intrinsic, odo_init,
         o3d.pipelines.odometry.RGBDOdometryJacobianFromHybridTerm(), option)

    if success_color_term:
        print("Using RGB-D Odometry")
        print(trans_color_term)
        source_pcd_color_term = o3d.geometry.PointCloud.create_from_rgbd_image(
            source_rgbd_image, pinhole_camera_intrinsic)
        source_pcd_color_term.transform(trans_color_term)
        o3d.visualization.draw([target_pcd, source_pcd_color_term])

    if success_hybrid_term:
        print("Using Hybrid RGB-D Odometry")
        print(trans_hybrid_term)
        source_pcd_hybrid_term = o3d.geometry.PointCloud.create_from_rgbd_image(
            source_rgbd_image, pinhole_camera_intrinsic)
        source_pcd_hybrid_term.transform(trans_hybrid_term)
        o3d.visualization.draw([target_pcd, source_pcd_hybrid_term])

robust_icp.py

27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81

import open3d as o3d
import numpy as np
import copy


def draw_registration_result(source, target, transformation):
    source_temp = copy.deepcopy(source)
    target_temp = copy.deepcopy(target)
    source_temp.paint_uniform_color([1, 0.706, 0])
    target_temp.paint_uniform_color([0, 0.651, 0.929])
    source_temp.transform(transformation)
    o3d.visualization.draw([source_temp, target_temp])


def apply_noise(pcd, mu, sigma):
    noisy_pcd = copy.deepcopy(pcd)
    points = np.asarray(noisy_pcd.points)
    points += np.random.normal(mu, sigma, size=points.shape)
    noisy_pcd.points = o3d.utility.Vector3dVector(points)
    return noisy_pcd


if __name__ == "__main__":
    pcd_data = o3d.data.DemoICPPointClouds()
    source = o3d.io.read_point_cloud(pcd_data.paths[0])
    target = o3d.io.read_point_cloud(pcd_data.paths[1])
    trans_init = np.asarray([[0.862, 0.011, -0.507, 0.5],
                             [-0.139, 0.967, -0.215, 0.7],
                             [0.487, 0.255, 0.835, -1.4], [0.0, 0.0, 0.0, 1.0]])

    # Mean and standard deviation.
    mu, sigma = 0, 0.1
    source_noisy = apply_noise(source, mu, sigma)

    print("Displaying source point cloud + noise:")
    o3d.visualization.draw([source_noisy])

    print(
        "Displaying original source and target point cloud with initial transformation:"
    )
    draw_registration_result(source, target, trans_init)

    threshold = 1.0
    print("Using the noisy source pointcloud to perform robust ICP.\n")
    print("Robust point-to-plane ICP, threshold={}:".format(threshold))
    loss = o3d.pipelines.registration.TukeyLoss(k=sigma)
    print("Using robust loss:", loss)
    p2l = o3d.pipelines.registration.TransformationEstimationPointToPlane(loss)
    reg_p2l = o3d.pipelines.registration.registration_icp(
        source_noisy, target, threshold, trans_init, p2l)
    print(reg_p2l)
    print("Transformation is:")
    print(reg_p2l.transformation)
    draw_registration_result(source, target, reg_p2l.transformation)