Overlapping Region Estimation-Based Point Cloud Registration

Extracting overlapping regions is crucial for point cloud registration. Recently, coarse-to-fine approaches have gained popularity. These methods rely on highly accurate superpoint correspondences, which are then propagated to denser points. However, these correspondences inevitably introduce non-overlapping region superpoints, leading to a decrease in superpoint matching accuracy. To tackle this challenge, we introduce the Overlapping Region Estimation-Based (ORE-Based) Point Cloud Registration method, a novel approach designed to enhance matching accuracy by reducing the influence of non-overlapping points. Our method utilizes a learning model to predict whether each superpoint is situated within overlapping regions. By excluding superpoints outside these regions during correspondence extraction, we achieve improved matching accuracy. This approach ensures high registration precision regardless of the overlap ratio between the point clouds being registered. By specifically targeting overlapping regions, our method significantly enhances the robustness and reliability of point cloud registration across diverse scenarios. Although simple, our approach achieves state-of-the-art performance on the challenging ModelLoNet benchmark, reducing Relative Rotation Error by 12.87 % and Relative Translation Error by 14.47 % under conditions of larger initial rotation error.