  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. Learning robust point cloud registration models with deep neural
networks has emerged as a powerful paradigm, offering promising performance in
predicting the global geometric transformation for a pair of point sets.
Existing methods first 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 the source point set to
the target one is then formed by the weighted fusion of region-aware
transformation. Compared to the state-of-the-art approaches, our experiments
show that our 3D-URRT achieves superior registration performance over various
benchmark datasets (e.g. ModelNet40).
