Robust probability model based on variational Bayes for point set registration

Abstract

Point set registration is widely used in various fields, but because the current registration algorithms suffer from the complexities of point set distributions, it has become a challenging problem. To solve this problem, we propose a probabilistic model based on variational Bayes. Specifically, we propose to build an asymmetric generalized Gaussian mixture probability model to evaluate the correspondence between point sets and eliminate outliers, by controlling the mixing ratio of the corresponding points to deal with the missing correspondence and using intermediate variable to simulate the transition from the model point set to the target point set. We propose a local variation to speed up the accurate update of the parameters to obtain a more compact lower bound of the change. In addition, a global–local strategy constraint transfer function is proposed, and coarse-to-fine registration is achieved by simulating the degradation scheme. Experimental results show that our method has the best robustness compared with the state-of-the-art registration algorithms.