Shi Chunhao, Wang Chunyang, Liu Xuelian, Sun Shaoyu, Xiao Bo, Li Xuemei, Li Guorui
Appl Opt. 2022 Feb 20;61(6):1331-1343. doi: 10.1364/AO.446913.
Point cloud noise is inevitable in the LiDAR scanning of objects and affects measurement accuracy and integrity. To minimize such noise, we propose a gravitational feature function-based point cloud denoising algorithm and a universal gravitation formula for a point cloud. First, we calculate the point cloud barycenter (i.e., the position of the average mass distribution) and the spherical neighborhood of points in terms of the distribution of the point cloud in three-dimensional space. Next, using the proposed formula, we calculate the gravitational forces between the barycenter and the spherical neighborhood of all points. We then combine all of the gravitational forces into a gravitational feature function and filter the noises in the point cloud using a gravitational feature-function threshold. This novel algorithm, to the best of our knowledge, effectively removes drift noises and takes into account the local and global structure of point clouds. Finally, we demonstrate the effectiveness of the algorithm through extensive experiments in which sparse, dense, and mixed noises are removed.
在对物体进行激光雷达扫描时，点云噪声是不可避免的，并且会影响测量精度和完整性。为了将此类噪声降至最低，我们提出了一种基于引力特征函数的点云去噪算法以及一个用于点云的万有引力公式。首先，我们根据点云在三维空间中的分布来计算点云质心（即平均质量分布的位置）以及点的球形邻域。接下来，使用所提出的公式，我们计算质心与所有点的球形邻域之间的引力。然后，我们将所有引力组合成一个引力特征函数，并使用引力特征函数阈值来过滤点云中的噪声。据我们所知，这种新颖的算法能够有效去除漂移噪声，并考虑到了点云的局部和全局结构。最后，我们通过大量去除稀疏、密集和混合噪声的实验来证明该算法的有效性。
