Robust Variational Bayesian Point Set Registration

Jie Zhou, Xinke Ma, Li Liang, Yang Yang, Shijin Xu, Yuhe Liu, Sim-Heng Ong; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2019, pp. 9905-9914

In this work, we propose a hierarchical Bayesian network based point set registration method to solve missing correspondences and various massive outliers. We construct this network first using the finite Student s t latent mixture model (TLMM), in which distributions of latent variables are estimated by a tree-structured variational inference (VI) so that to obtain a tighter lower bound under the Bayesian framework. We then divide the TLMM into two different mixtures with isotropic and anisotropic covariances for correspondences recovering and outliers identification, respectively. Finally, the parameters of mixing proportion and covariances are both taken as latent variables, which benefits explaining of missing correspondences and heteroscedastic outliers. In addition, a cooling schedule is adopted to anneal prior on covariances and scale variables within designed two phases of transformation, it anneal priors on global and local variables to perform a coarse-to- fine registration. In experiments, our method outperforms five state-of-the-art methods in synthetic point set and realistic imaging registrations.