Robust Partial-to-Partial Point Cloud Registration with Overlapping Mask Learning

Point cloud registration is a key task in many computational fields. Previous correspondence matching-based methods require the inputs to have distinctive geometric structures to fit a 3D rigid transformation according to point-wise sparse feature matches. However, the accuracy of transformation heavily relies on the quality of extracted features, which are prone to errors with respect to partiality and noise. In addition, they can not utilize the geometric knowledge of all the overlapping regions. On the other hand, previous global feature-based approaches can utilize the entire point cloud for the registration; however, they ignore the negative effect of non-overlapping points when aggregating global features. In this paper, we present a global feature-based iterative network for partial-to-partial point cloud registration. We learn overlapping masks to reject non-overlapping regions, which converts the partial-to-partial registration to the registration of the same shape. Moreover, the previously used data is sampled only once from the CAD models for each object, resulting in the same point clouds for the source and reference. We propose a more practical manner of data generation where a CAD model is sampled twice for the source and reference, avoiding the previously prevalent over-fitting issue. Experiments show that our method achieves state-of-the-art performance compared to traditional and deep learning-based methods. Furthermore, extensive evaluations on real-world datasets, including Stanford 3D Scanning, 3DMatch, and KITTI, demonstrate that our method generalizes well to real-world object scans, indoor scenes, and outdoor scenarios. Code is available on https: //github. com/hxwork/OMNetPytorch.