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PRNet: Self-supervised learning for partial-to-partial registration
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© 2019 Neural information processing systems foundation. All rights reserved. We present a simple, flexible, and general framework titled Partial Registration Network (PRNet), for partial-to-partial point cloud registration. Inspired by recently-proposed learning-based methods for registration, we use deep networks to tackle non-convexity of the alignment and partial correspondence problems. While previous learning-based methods assume the entire 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 geometric representation, a keypoint detector that finds points in common between partial views, and keypoint-to-keypoint correspondences. We show PRNet predicts keypoints and correspondences consistently across views and objects. Furthermore, the learned representation is transferable to classification.
Date issued
2019-12Journal
Advances in Neural Information Processing Systems
Citation
2019. "PRNet: Self-supervised learning for partial-to-partial registration." Advances in Neural Information Processing Systems, 32.
Version: Final published version