Bayesian rigid point set registration using logarithmic double exponential prior

Abstract

Point set registration is a key problem in many computer vision tasks. The goal of point set registration is to match two sets of points and estimate the transformation parameter that maps one point set to the other. Among the many published registration methods, the recently proposed Coherent Point Drift (CPD) algorithm stands out for its accuracy. In this paper we show that by casting CPD in the Bayesian framework we can obtain even better results. In particular, in case of large translation amount, our proposed mathod has much less number of iterations than CPD without any loss of accuracy. Experimental results confirms the advantages of the proposed method and shows an overall speedup when compared with the CPD method.