Research on point cloud segmentation using a minimum spanning tree
应用最小生成树实现点云分割

Point cloud segmentation is widely used in point cloud parameterization, shape recognition, and model editing. A point cloud segmentation algorithm based on a minimum spanning tree is proposed, which includes four steps: generating banded segmentation boundaries,region growing, splitting banded boundaries, and generating the final regions. The Snake model is used to extract the dividing lines, and the lines are expanded towards both sides to generate banded segmentation boundaries. Then the Minimum Spanning Tree is used to extract all interior points in each region using region growing. At the last step, the banded segmentation boundaries are split to several parts, and each part combined with its region to generate the final regions. Experiments show that the algorithm can avoid over segmentation or under segmentation and generate smooth segmentation boundaries. Compared with the Level Set segmentation algorithm, the algorithm can segment point cloud more efficiently.