Partial-to-Partial Point Cloud Registration by Rotation Invariant Features and Spatial Geometric Consistency

Abstract

Point cloud registration is a critical problem in 3D vision tasks, and numerous learning-based point cloud registration methods have been proposed in recent years. However, a common issue with most of these methods is that their feature descriptors are rotation-sensitive, which makes them difficult to converge at large rotations. In this paper, we propose a new learning-based pipeline to address this issue, which can also handle partially overlapping 3D point clouds. Specifically, we employ rotation-invariant local features to guide the point matching task, and utilize a cross-attention mechanism to update the feature information between the two point clouds to predict the key points in the overlapping regions. Subsequently, we construct a feature matrix based on the features of the key points to solve the soft correspondences. Finally, we construct a non-learning correspondence constraint module that exploits the spatial geometric invariance of the point clouds after rotation and translation, as well as the compatibility between point pairs, to reject the wrong correspondences. To validate our approach, we conduct extensive experiments on ModelNet40. Our approach achieves better performance compared to other methods, especially in the presence of large rotations.

Keywords

• point cloud registration
• deep learning
• spatial geometric consistency
• rotation-invariant descriptors