3D Unsupervised Region-Aware Registration Transformer

This paper concerns the research problem of point cloud registration to find the rigid transformation to optimally align the source point set with the target one. Existing methods firstly leverage an encoder to regress the global shape descriptor, which is then decoded into a shape-conditioned transformation via concatenation-based conditioning. However, different regions of a 3D shape vary in their geometric structures which makes it more sense that we have a region-conditioned transformation instead of the shape-conditioned one. In this paper we define our 3D registration function through the introduction of a new design of 3D region partition module that is able to divide the input shape to different regions with a self-supervised 3D shape reconstruction loss without the need for ground truth labels. We further propose the 3D shape transformer module to efficiently and effectively capture short- and long-range geometric dependencies for regions on the 3D shape Consequently, the region-aware decoder module is proposed to predict the transformations for different regions respectively. The global geometric transformation from source point set to target one is then formed by the weighted fusion of region-aware transformation.