GOR-PCR: Graph-based Outlier Removal via Global-to-Local Consistency for Point Cloud Registration

In point cloud registration, the point cloud with much noise or low overlap will cause more outliers, consequently leading to registration failure. This paper introduces a graph-based outlier removal method with global-to-local consistency for robust point cloud registration. To address the issue of global ambiguity inherent in second-order consistency, we introduce the concept of global-to-local outlier removal. This approach preserves the outlier filtering capability of global consistency while mitigating the ambiguity associated with global consistency. Furthermore, local consistency enhances the detection of local overlapping regions. To bolster the robustness of local consistency in identifying outliers, we propose a dual criterion based on both distance and angle. Finally, robust global registration is achieved by overlap-aware hypothesis verification. Our training-free method outperforms other outlier removal methods, achieving state-of-the-art results of 96.80% RR and 93.79% IR on the 3DMatch dataset. In the outdoor KITTI dataset, our method demonstrates the capability to achieve robust and accurate registration using the less informative FPFH descriptor, attaining the highest RR of 99.82% and the lowest RE of 8.11. Additionally, this method can be effectively integrated with learning-based point cloud registration, which significantly improves their registration recall without introducing much time and space overhead. Moreover, our method improves the feature-based methods by 1.0 ∼ 29.9 pp on RR and 8.0 ∼ 51.3 pp on IR. The codes will be released soon.