End-to-End Point Cloud Registration via Rotation Equivariant Descriptors

Abstract

Point cloud registration (PCR) aims to recover the rigid transformation between two noisy, unordered point sets. This task is typically tackled by establishing point-wise correspondences, and solving the rigid transformation between the two sets. Since descriptor-based methods find correspondences by matching the feature space distance, a powerful and rotation-robust point feature extractor is critical to the success of this task. Existing methods assume soft rotation invariance/equivariance through the means of training augmentation, rotational discretization or pre-alignment of patches. In contrast, this paper proposes a new method which generates fully rotation invariant and equivariant descriptors by construction. For each keypoint patch, our network extracts not only a rotation invariant descriptor for establishing corre-spondences, but also a rotation equivariant one. The rotation equivariant descriptor allows relative transformation to be directly recovered from a single correspondence pair, unlike standard methods that require three correspondences. This design significantly reduces iteration number of RANSAC and guarantees high registration recall when the inlier ratio of estimated correspondences is low. Extensive experiments have demonstrated that the proposed method outperforms state-of-art methods in the same category even after much fewer RANSAC iterations.