  Point cloud registration is the process of aligning a pair of point sets via
searching for a geometric transformation. Unlike classical optimization-based
methods, recent learning-based methods leverage the power of deep learning for
registering a pair of point sets. In this paper, we propose to develop a novel
model that organically integrates the optimization to learning, aiming to
address the technical challenges in 3D registration. More specifically, in
addition to the deep transformation decoding network, our framework introduce
an optimizable deep \underline{S}patial \underline{C}orrelation
\underline{R}epresentation (SCR) feature. The SCR feature and weights of the
transformation decoder network are jointly updated towards the minimization of
an unsupervised alignment loss. We further propose an adaptive Chamfer loss for
aligning partial shapes. To verify the performance of our proposed method, we
conducted extensive experiments on the ModelNet40 dataset. The results
demonstrate that our method achieves significantly better performance than the
previous state-of-the-art approaches in the full/partial point set registration
task.
