（13）点云数据处理学习——点对面ICP注册过程展示

1、主要参考

（1）open3d的例子

non_blocking_visualization.py这个文件

 （2）视频

Point Clouds Visualization and Animation - Point Cloud Processing in Open3D wit_哔哩哔哩_bilibili

2、代码实现

 2.1代码

# ----------------------------------------------------------------------------
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# examples/python/visualization/non_blocking_visualization.py

import open3d as o3d
import numpy as np

if __name__ == "__main__":
    o3d.utility.set_verbosity_level(o3d.utility.VerbosityLevel.Debug)
    pcd_data = o3d.data.DemoICPPointClouds()
    source_raw = o3d.io.read_point_cloud(pcd_data.paths[0])
    target_raw = o3d.io.read_point_cloud(pcd_data.paths[1])

    source = source_raw.voxel_down_sample(voxel_size=0.02)
    target = target_raw.voxel_down_sample(voxel_size=0.02)
    trans = [[0.862, 0.011, -0.507, 0.0], [-0.139, 0.967, -0.215, 0.7],
             [0.487, 0.255, 0.835, -1.4], [0.0, 0.0, 0.0, 1.0]]
    source.transform(trans)

    flip_transform = [[1, 0, 0, 0], [0, -1, 0, 0], [0, 0, -1, 0], [0, 0, 0, 1]]
    source.transform(flip_transform)
    target.transform(flip_transform)

    vis = o3d.visualization.Visualizer()
    vis.create_window()
    vis.add_geometry(source)
    vis.add_geometry(target)
    threshold = 0.05
    icp_iteration = 100
    save_image = False

    for i in range(icp_iteration):
        reg_p2l = o3d.pipelines.registration.registration_icp(
            source, target, threshold, np.identity(4),
            o3d.pipelines.registration.TransformationEstimationPointToPlane(),
            o3d.pipelines.registration.ICPConvergenceCriteria(max_iteration=1))
        source.transform(reg_p2l.transformation)
        vis.update_geometry(source)
        vis.poll_events()
        vis.update_renderer()
        if save_image:
            vis.capture_screen_image("temp_%04d.jpg" % i)
    vis.destroy_window()
    o3d.utility.set_verbosity_level(o3d.utility.VerbosityLevel.Info)

2.2测试结果

会动态展示匹配过程：）