A Partial-to-Partial Point Cloud Registration Method Based on Geometric Attention Network

Abstract

Partial point cloud registration is an important step in generating a full 3D model. Many deep learning-based methods show good performance for the registration of complete point clouds but cannot deal with the registration of partial point clouds effectively. Recent methods that seek correspondences over downsampled superpoints show great potential in partial point cloud registration. Therefore, this paper proposes a partial-to-partial point cloud registration network based on geometric attention (GAP-Net), which mainly includes a backbone network optimized by a spatial attention module and an overlapping attention module guided by geometric information. The former aggregates the feature information of superpoints, and the latter focuses on superpoint matching in overlapping regions. The experimental results show that the method achieves better registration performance on ModelNet and ModelLoNet with lower overlap. The rotation error is reduced by 14.49% and 17.12%, respectively, which is robust to the overlap rate.