Pipelines

colored_pointcloud_registration.py

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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)

    # 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)

icp_registration.py

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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

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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

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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

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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

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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

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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

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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

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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)