Message Passing Matching Dynamics for Overlapping Point Identification

Abstract

Existing registration algorithms usually converge to a local minimum due to inaccurate evaluation of the tentative correspondences established. In this paper, we move a step further and instead estimate the extent to which a point lies in the overlapping area. To this end, we regard the registration problem as an exchange network and develop a matching dynamics to characterize the interaction inside. Then we propose a novel algorithm based on the powerful message passing scheme derived from the matching dynamics for the optimization of the overlapping point weight. The novel algorithm penalizes in the process of deterministic annealing those tentative correspondences that violate the properties of the matching dynamics. The rigid transformation that brings the two overlapping shapes into alignment is finally estimated in the weighted least squares sense. Our experiments use both synthetic and real data to show that our proposed algorithm is more likely to converge to the global minimum than four selected state of the art ones for more accurate and robust results.