ViGG: Robust RGB-D Point Cloud Registration using Visual-Geometric Mutual Guidance
This addresses the challenge of robust registration for 3D vision applications, offering a novel approach that enhances practical applicability in tasks like robotics and augmented reality.
The paper tackled the problem of RGB-D point cloud registration by proposing ViGG, a method that uses visual-geometric mutual guidance to improve robustness, and it outperformed state-of-the-art methods on datasets like 3DMatch, ScanNet, and KITTI.
Point cloud registration is a fundamental task in 3D vision. Most existing methods only use geometric information for registration. Recently proposed RGB-D registration methods primarily focus on feature fusion or improving feature learning, which limits their ability to exploit image information and hinders their practical applicability. In this paper, we propose ViGG, a robust RGB-D registration method using mutual guidance. First, we solve clique alignment in a visual-geometric combination form, employing a geometric guidance design to suppress ambiguous cliques. Second, to mitigate accuracy degradation caused by noise in visual matches, we propose a visual-guided geometric matching method that utilizes visual priors to determine the search space, enabling the extraction of high-quality, noise-insensitive correspondences. This mutual guidance strategy brings our method superior robustness, making it applicable for various RGB-D registration tasks. The experiments on 3DMatch, ScanNet and KITTI datasets show that our method outperforms recent state-of-the-art methods in both learning-free and learning-based settings. Code is available at https://github.com/ccjccjccj/ViGG.