point_cloud_outlier_removal.py

import open3d as o3d
import numpy as np

if __name__ == '__main__':
    # Read point cloud:
    pcd = o3d.io.read_point_cloud("../data/depth_2_pcd.ply")
    # Down sampling to reduce the running time:
    pcd = pcd.voxel_down_sample(voxel_size=0.02)

    # Radius outlier removal:
    pcd_rad, ind_rad = pcd.remove_radius_outlier(nb_points=16, radius=0.05)
    outlier_rad_pcd = pcd.select_by_index(ind_rad, invert=True)
    outlier_rad_pcd.paint_uniform_color([1., 0., 1.])

    # Statistical outlier removal:
    pcd_stat, ind_stat = pcd.remove_statistical_outlier(nb_neighbors=20,
                                                 std_ratio=2.0)
    outlier_stat_pcd = pcd.select_by_index(ind_stat, invert=True)
    outlier_stat_pcd.paint_uniform_color([0., 0., 1.])

    # Translate to visualize:
    points = np.asarray(pcd_stat.points)
    points += [3, 0, 0]
    pcd_stat.points = o3d.utility.Vector3dVector(points)

    points = np.asarray(outlier_stat_pcd.points)
    points += [3, 0, 0]
    outlier_stat_pcd.points = o3d.utility.Vector3dVector(points)

    # Display:
    o3d.visualization.draw_geometries([pcd_stat, pcd_rad, outlier_stat_pcd, outlier_rad_pcd])