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# ----------------------------------------------------------------------------
# - Open3D: www.open3d.org -
# ----------------------------------------------------------------------------
# Copyright (c) 2018-2024 www.open3d.org
# SPDX-License-Identifier: MIT
# ----------------------------------------------------------------------------
import os
import sys
import pickle
import open3d as o3d
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 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):
source = o3d.io.read_point_cloud(dataset.paths[s])
source_down, source_fpfh = preprocess_point_cloud(source, voxel_size)
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