  3D point cloud registration in remote sensing field has been greatly advanced
by deep learning based methods, where the rigid transformation is either
directly regressed from the two point clouds (correspondences-free approaches)
or computed from the learned correspondences (correspondences-based
approaches). Existing correspondences-free methods generally learn the holistic
representation of the entire point cloud, which is fragile for partial and
noisy point clouds. In this paper, we propose a correspondences-free
unsupervised point cloud registration (UPCR) method from the representation
separation perspective. First, we model the input point cloud as a combination
of pose-invariant representation and pose-related representation. Second, the
pose-related representation is used to learn the relative pose wrt a "latent
canonical shape" for the source and target point clouds respectively. Third,
the rigid transformation is obtained from the above two learned relative poses.
Our method not only filters out the disturbance in pose-invariant
representation but also is robust to partial-to-partial point clouds or noise.
Experiments on benchmark datasets demonstrate that our unsupervised method
achieves comparable if not better performance than state-of-the-art supervised
registration methods.
