Generalized 3D Rigid Point Set Registration with Anisotropic Positional Error Based on Bayesian Coherent Point Drift

Abstract

This paper presents a novel, robust, and accurate three-dimensional (3D) rigid point set registration (PSR) method, which is achieved by generalizing the state-of-the-art (SOTA) Bayesian coherent point drift (BCPD) theory to the scenario that high-dimensional point sets(PSs) are aligned and that the anisotropic positional noise is considered. Our contributions in this paper are three folds. First, the problem of rigidly aligning two general point sets (PSs) with normal vectors is incorporated into a variational Bayesian inference framework, which is solved by generalizing the BCPD approach while the anisotropic positional noise is considered. Second, the updated parameters during the algorithm's iterations are given in closed-form or iterative solutions. Third, extensive experiments have been done to validate the proposed approach and its significant improvements over the BCPD.