A Boundary-Enhanced Supervoxel Method for 3D Point Clouds
Zhengchuan Sha, Qing Zhu, Yiping Chen, Cheng Wang, Abdul Nurunnabi, Jonathan Li · 2020
This paper presents a boundary-enhanced supervoxel method to solve over-segmentation problems in supervoxel generation of Voxel Cloud Connectivity Segmentation (VCCS). First, we use different searching methods to obtain the neighborhood of each point. Second, three variants of neighbor points are clustered by the local k-means clustering method on points directly instead of on voxels. Finally, a scale metric is used to measure the difference between two points that considers underlying 3D spatial structure of the points. Our proposed is tested on two publicly available benchmark point cloud datasets acquired by mobile laser scanning (MLS) and terrestrial laser scanning (TLS) systems, respectively. Results of the experiments show that the boundary recall approximately 7 and 4 times higher than VCCS for the best results, which our proposed methods are effective, and the cost time is feasible and effective.