| import open3d as o3d | |
| import numpy as np | |
| def downsample_and_filter(pcd_file): | |
| pcd = o3d.io.read_point_cloud(pcd_file, max_bound_div = 750, neighbor_num = 8) | |
| point_num = len(pcd.points) | |
| if (point_num > 10000000): | |
| voxel_down_pcd = o3d.geometry.PointCloud.uniform_down_sample(pcd, int(point_num / 10000000)+1) | |
| else: | |
| voxel_down_pcd = pcd | |
| max_bound = voxel_down_pcd.get_max_bound() | |
| ball_radius = np.linalg.norm(max_bound) / max_bound_div | |
| pcd_filter, _ = voxel_down_pcd.remove_radius_outlier(neighbor_num, ball_radius) | |
| print('filtered size', len(pcd_filter.points), 'pre size:', len(pcd.points)) | |
| o3d.io.write_point_cloud(pcd_file[:-4] + '_filtered.ply', pcd_filter) | |
| if __name__ == "__main__": | |
| import os | |
| dir_path = './data/demo_pcd' | |
| for pcd_file in os.listdir(dir_path): | |
| #if 'jonathan' in pcd_file: set max_bound_div to 300 and neighbot_num to 8 | |
| downsample_and_filter(os.path.join(dir_path, pcd_file)) | |