3DPCP-Net: A Lightweight Progressive 3D Correspondence Pruning Network for Accurate and Efficient Point Cloud Registration

Accurately identifying correct correspondence (inlier) within initial ones is pivotal for robust feature-based point cloud registration. Current methods typically rely on one-shot 3D correspondence classification with a single coherence constraint to obtain inlier. These approaches are either insufficiently accurate or inefficient, often requiring more network parameters. To address this issue, we propose a lightweight network, 3DPCP-Net, for fast and robust registration. Its core design lies in progressive correspondence pruning through mining deep spatial geometric coherence, which can effectively learn pairwise 3D spatial distance and angular features to progressively remove outlier (mismatched correspondence) for accurate pose estimation. Moreover, we also propose an efficient feature-based hypothesis proposer that leverages the geometric consistency features to generate reliable model hypotheses for each reliable correspondence explicitly. Extensive experiments on 3DMatch, 3DLoMatch, KITTI and Augmented ICL-NUIM demonstrate the accurate and efficient of our method for outlier removal and pose estimation tasks. Furthermore, our method is highly versatile and can be easily integrated into both learning-based and geometry-based frameworks, enabling them to achieve state-of-the-art results. Code is available at https://github.com/jtw220/3DPCP-Net.