GIREG: Geometric-Image Collaborative Point Cloud Regisration

Point cloud registration is an essential task in 3D computer vision. However, for repetitive patterns and low-geometric region in indoor scenarios, most existing methods cannot distinguish real useful features, and filter out incorrect correspondences effectively. To tackle this challenge, we propose a geometric-image collaborative point cloud registration approch, so-called GIReg. We perform pixel-wise and patch-wise feature extraction (P2FE) of images, and design a superpoint feature representation module to enhance learned 3D point cloud through a two-stage fusion of geometric and image features. Besides, through a geometric and image feature correlation via attention guidance (GIFCAG) module, the global correlation of geometric and image within a point cloud, and the consistency of geometric and image between point cloud are captured. Finally, the distinctive superpoint features are used for registration. Extensive experiments demonstrate that our method achieves superior performance, especially in the geometrically challenging scenarios.