Non-Rigid Multiple Point Set Registration Using Latent Gaussian Mixture

Abstract

Point set registration is a fundamental task in 3D computer vision. Existing registration approaches mainly focus on either pair-wise or rigid registration. In this paper, we propose a robust group-wise registration method from a probabilistic view and adopt non-rigid transformations to register multiple point sets without bias toward any given set. The proposed method lessens the need of point correspondences by representing each point set as Gaussian Mixture Model and the registration is equivalent to multiple distributions alignment. Closed-form of Jensen-Rényi divergence and L 2 distance are used as cost functions. We further design a neural network to extract correspondences between raw point sets and Gaussian Mixture Model (GMM) parameters, and recover the optimal diffeomorphic non-rigid transformations from the matched GMM parameters. The proposed method is compared against two well-known probabilistic methods for group-wise point-set registration on several public 2D and 3D datasets. The results demonstrate that our method improves registration accuracy.