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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 os
import sys
import numpy as np
pyexample_path = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
sys.path.append(pyexample_path)
from open3d_example import *
do_visualization = False
def execute_fast_global_registration(source_down, target_down, source_fpfh,
target_fpfh, voxel_size):
distance_threshold = voxel_size * 0.5
print(":: Apply fast global registration with distance threshold %.3f" \
% distance_threshold)
result = o3d.pipelines.registration.registration_fgr_based_on_feature_matching(
source_down, target_down, source_fpfh, target_fpfh,
o3d.pipelines.registration.FastGlobalRegistrationOption(
maximum_correspondence_distance=distance_threshold))
return result
def preprocess_point_cloud(pcd, voxel_size):
print(":: Downsample with a voxel size %.3f." % voxel_size)
pcd_down = pcd.voxel_down_sample(voxel_size)
radius_normal = voxel_size * 2
print(":: Estimate normal with search radius %.3f." % radius_normal)
pcd_down.estimate_normals(
o3d.geometry.KDTreeSearchParamHybrid(radius=radius_normal, max_nn=30))
radius_feature = voxel_size * 5
print(":: Compute FPFH feature with search radius %.3f." % radius_feature)
pcd_fpfh = o3d.pipelines.registration.compute_fpfh_feature(
pcd_down,
o3d.geometry.KDTreeSearchParamHybrid(radius=radius_feature, max_nn=100))
return pcd_down, pcd_fpfh
if __name__ == "__main__":
# data preparation
dataset = o3d.data.LivingRoomPointClouds()
n_ply_files = len(dataset.paths)
voxel_size = 0.05
alignment = []
for s in range(n_ply_files):
for t in range(s + 1, n_ply_files):
print("LivingRoomPointClouds:: matching %d-%d" % (s, t))
source = o3d.io.read_point_cloud(dataset.paths[s])
target = o3d.io.read_point_cloud(dataset.paths[t])
source_down, source_fpfh = preprocess_point_cloud(
source, voxel_size)
target_down, target_fpfh = preprocess_point_cloud(
target, voxel_size)
result = execute_fast_global_registration(source_down, target_down,
source_fpfh, target_fpfh,
voxel_size)
if (result.transformation.trace() == 4.0):
success = False
else:
success = True
# Note: we save inverse of result_ransac.transformation
# to comply with http://redwood-data.org/indoor/fileformat.html
alignment.append(
CameraPose([s, t, n_ply_files],
np.linalg.inv(result.transformation)))
print(np.linalg.inv(result.transformation))
if do_visualization:
draw_registration_result(source_down, target_down,
result.transformation)
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