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.