关键词: 点云分割, k-d 树, 随机霍夫变换（RHT）, 区域生长

Abstract: Planar objects detection is the key approach for LIDAR data and 3D reconstruction now. Basic building types are analysed. k-d tree is used to create adjacency information of points; randomized Hough Transform is used to detect and fit the planar objects; region growing approach based on geometrical similarity and colorimetrical similarity is presented. The segmentation algorithm requires a small number of manually set parameters which are used to keep balance between under- and over-segmentation. The experiments of the presented algorithm on a point cloud of architecture show its effectiveness.

Key words: point cloud segmentation, k-d tree, Randomized Hough Transform, region growing