Graph-matching-based correspondence search for nonrigid point cloud registration

Nonrigid registration finds transformations to fit a source point cloud/mesh to a target point cloud/mesh. Most nonrigid registration algorithms consist of two steps; finding correspondence and optimization. Among these, finding correspondence plays an important role in registration performance. However, when two point clouds have large displacement, it is hard to know correct correspondences and an algorithm often fails to find correct transformations. In this paper, we propose a novel graph-matching-based correspondence search for nonrigid registration and a corresponding optimization method for finding transformation to complete nonrigid registration. Considering global connectivity as well as local similarity for the correspondence search, the proposed method finds good correspondences according to semantics and consequently finds correct transformations even when the motion is large. Our algorithm is experimentally validated on human body and animal datasets, which verifies that it is capable of finding correct transformations to fit a source to a target.