Rotation robust non-rigid point set registration with Bayesian student's t mixture model

Aiming to improve the performance of non-rigid point set registration, this paper proposes a probabilistic method with student's t mixture model (SMM) under the Bayesian inference framework. In the proposed method, non-rigid point set registration is formulated as a probabilistic density estimation problem with SMM in Bayesian manner. In order to improve the robustness to rotation degradation, we consider the rotation transformation in modeling non-rigid displacement. Then, the hierarchical Bayesian model of non-rigid point set registration is constructed, and approximate posteriors of model parameters are derived by the variational Bayesian Expectation Maximization update rules, which can provide the uncertainty measurements of parameters. For those parameters without priors imposed, the updating formulae are obtained by directly maximizing variational lower bound. Finally, an empirical coarse-to-fine algorithm is designed to perform non-rigid point set registration process. The experimental results demonstrate that the proposed method can achieve higher matching performance compared with other several state-of-the-art registration methods.