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# ----------------------------------------------------------------------------
# - Open3D: www.open3d.org -
# ----------------------------------------------------------------------------
# The MIT License (MIT)
#
# Copyright (c) 2018-2021 www.open3d.org
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
# IN THE SOFTWARE.
# ----------------------------------------------------------------------------
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])
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