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We present a simple, flexible, and general framework titled PartialRegistration Network (PRNet), for partial-to-partial point cloud registration.Inspired by recently-proposed learning-based methods for registration, we usedeep networks to tackle non-convexity of the alignment and partialcorrespondence problems. While previous learning-based methods assume theentire shape is visible, PRNet is suitable for partial-to-partial registration,outperforming PointNetLK, DCP, and non-learning methods on synthetic data.PRNet is self-supervised, jointly learning an appropriate geometricrepresentation, a keypoint detector that finds points in common between partialviews, and keypoint-to-keypoint correspondences. We show PRNet predictskeypoints and correspondences consistently across views and objects.Furthermore, the learned representation is transferable to classification.

PRNet: Self-Supervised Learning for Partial-to-Partial Registration