关键词: 超体素, 法向量, 区域增长, 点云分割

Abstract: Point cloud segmentation is the basis of point cloud recognition and modeling. In order to improve the accuracy and efficiency of point cloud segmentation, a super voxel based on semiautomatic method for region growing segmentation of three-dimensional point cloud is proposed. First of all, the original point cloud is divided into super voxels by using Octree according to the spatial location and normal vector of the point clouds. Secondly, a new uniform density point cloud is built by using all the central units of super voxels. In this case, the number of original point clouds decreases, which leads to a reduced processing time. Then, K-D tree is utilized as data structure to organize the resampled cloud points, and the points are merged into clusters constrained by local connectivities and smoothness. Finally, all the segmented point clouds are up-sampled back to the original point clouds. The proposed methodology is implemented on LiDAR point clouds from three?plant phenological periods. The quantitative assessment results demonstrate that the average fitting degree of the segmented point cloud through the proposed methodology and the manual segmentation reaches 93.38%, and the results also show high accuracy and high efficiency.

Key words: super voxels, normal vector, region growing, point cloud segmentation