RPM-Net

RPM-Net is an end-to-end differentiable deep network for robust point matching uses learned features. It preserves robustness of RPM against noisy/outlier points while desensitizing initialization with point correspondences from learned feature distances instead of spatial distances. The network uses the differentiable Sinkhorn layer and annealing to get soft assignments of point correspondences from hybrid features learned from both spatial coordinates and local geometry. To further improve registration performance, the authors introduce a secondary network to predict optimal annealing parameters.