Non-rigid point set registration by high-dimensional representation

Non-rigid point set registration is a key component in many computer vision and pattern recognition tasks. In this study, the authors propose a robust non-rigid point set registration method based on high-dimensional representation. Their central idea is to map the point sets into a high-dimensional space by integrating the relative structure information into the coordinates of points. On the one hand, the point set registration is formulated as the estimation of a mixture of densities in high-dimensional space. On the other hand, the relative distances are used to compute the local features which assign the membership probabilities of the mixture model. The proposed model captures discriminative relative information and enables to preserve both global and local structures of the point set during matching. Extensive experiments on both synthesised and real data demonstrate that the proposed method outperforms the state-of-the-art methods under various types of distortions, especially for the deformation and rotation degradations.