用视觉与几何互引导，提升RGB-D点云配准的鲁棒性
ViGG: Robust RGB-D Point Cloud Registration using Visual-Geometric Mutual Guidance
- 通过视觉-几何联合方式解决团块对齐，几何引导抑制模糊匹配
- 利用视觉先验缩小搜索空间，提取抗噪高质量对应点
- 在3DMatch、ScanNet等数据集上超越现有方法，适合实际应用
点云配准是3D视觉的基础任务。现有方法多仅依赖几何信息，而近期的RGB-D配准方法主要关注特征融合或学习改进，难以充分挖掘图像信息，限制了实用性。本文提出ViGG，一种基于视觉-几何互引导的鲁棒RGB-D配准方法。首先，以视觉-几何组合形式求解团块对齐，通过几何引导设计抑制模糊团块；其次，为缓解视觉匹配噪声导致的精度下降，提出视觉引导的几何匹配方法，利用视觉先验确定搜索范围，从而提取高质量、抗噪的对应关系。该互引导策略显著提升方法鲁棒性，适用于多种RGB-D配准任务。在3DMatch、ScanNet和KITTI数据集上的实验表明，本方法在无学习与有学习设置下均优于最新技术。代码已开源：https://github.com/ccjccjccj/ViGG。
原文摘要 · Abstract (English)
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.
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