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# ----------------------------------------------------------------------------
# - Open3D: www.open3d.org -
# ----------------------------------------------------------------------------
# Copyright (c) 2018-2024 www.open3d.org
# SPDX-License-Identifier: MIT
# ----------------------------------------------------------------------------
import open3d as o3d
import numpy as np
from copy import deepcopy
import argparse
def visualize_registration(src, dst, transformation=np.eye(4)):
src_trans = deepcopy(src)
src_trans.transform(transformation)
src_trans.paint_uniform_color([1, 0, 0])
dst_clone = deepcopy(dst)
dst_clone.paint_uniform_color([0, 1, 0])
o3d.visualization.draw([src_trans, dst_clone])
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()
# yapf: disable
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=100000,
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",
)
parser.add_argument(
"--method", choices=["from_features", "from_correspondences"], default="from_correspondences"
)
# yapf: enable
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)
if args.method == "from_features":
print("Running RANSAC from features")
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),
)
visualize_registration(src, dst, result.transformation)
elif args.method == "from_correspondences":
print("Running RANSAC from correspondences")
# Mimic importing customized external features (e.g. learned FCGF features) in numpy
# shape: (feature_dim, num_features)
src_fpfh_np = np.asarray(src_fpfh.data).copy()
dst_fpfh_np = np.asarray(dst_fpfh.data).copy()
src_fpfh_import = o3d.pipelines.registration.Feature()
src_fpfh_import.data = src_fpfh_np
dst_fpfh_import = o3d.pipelines.registration.Feature()
dst_fpfh_import.data = dst_fpfh_np
corres = o3d.pipelines.registration.correspondences_from_features(
src_fpfh_import, dst_fpfh_import, args.mutual_filter)
result = o3d.pipelines.registration.registration_ransac_based_on_correspondence(
src_down,
dst_down,
corres,
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),
)
visualize_registration(src, dst, result.transformation)
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