Abstract: Point cloud segmentation is a key processing step for identifying spatial features of geographic scenes, exploring and recording spatial information, and its segmentation accuracy directly affects the effects of subsequent 3D scene reconstruction and feature extraction. Aiming at the instability of traditional region-growing point cloud segmentation algorithms, this paper combines supervoxels and region-growing algorithms to segment point cloud data, and uses the color information of the point cloud itself to further improve the segmentation results. The experimental results show that compared to the traditional region growing and existing segmentation algorithms, combining supervoxels and region growing algorithm proposed in this paper has better effect on point cloud data segmentation, and its accuracy and recall rates are both improved.

Key words: supervoxel, region growth, color information, point cloud segmentation