  An unsupervised point cloud registration method, called salient points
analysis (SPA), is proposed in this work. The proposed SPA method can register
two point clouds effectively using only a small subset of salient points. It
first applies the PointHop++ method to point clouds, finds corresponding
salient points in two point clouds based on the local surface characteristics
of points and performs registration by matching the corresponding salient
points. The SPA method offers several advantages over the recent deep learning
based solutions for registration. Deep learning methods such as PointNetLK and
DCP train end-to-end networks and rely on full supervision (namely, ground
truth transformation matrix and class label). In contrast, the SPA is
completely unsupervised. Furthermore, SPA's training time and model size are
much less. The effectiveness of the SPA method is demonstrated by experiments
on seen and unseen classes and noisy point clouds from the ModelNet-40 dataset.

Computer Vision and Pattern Recognition