Maximum Spanning Tree for 3D Point Cloud Registration

Xin Zhao, Chengzhuan Yang, Zhonglong Zheng

Abstract: 3D point cloud registration, as a fundamental and challenging problem in computational vision, aims to seek the best pose to align a point cloud between two 3D objects or scenes and transform them into the same coordinate system to achieve the best alignment of point clouds. The key to point cloud registration is accurately finding the corresponding relationship between point clouds. In this paper, we propose a pure geometric 3D point cloud registration method based on the maximum spanning tree (MST): 1) We extract the local features of the point clouds for initial alignment and construct the initial compatibility graph of two 3D objects or scenes. 2) We sparse the initial compatibility graph and find the maximum spanning tree in the sparse graph. 3) We select the maximum spanning tree with the most nodes and use the DFS algorithm to find all the node sets(each node set contains three nodes) in the selected maximum spanning tree. 4) We generate and evaluate the hypothesis of the node sets, and the hypothesis with the highest evaluation score is the transform matrix solved. We extensively tested our method on the 3DMatch, 3DLoMatch datasets. The experimental results show that the proposed MST method performs the same or better than the existing 3D point cloud registration methods.