Robust real-world point cloud registration by inlier detection

Real-world point cloud registration is challenging because of large outliers in correspondence search. The mixture variations, such as partial overlap, noise and cross sources, are the root cause of these large outliers. Existing methods face challenges in effectively removing the large outliers. We propose a novel coarse-to-fine framework to remove the outliers by detecting the accurate inlier correspondences. Specifically, our coarse module predicts the top-K accurate correspondences. The coarse module is trained by jointly leveraging global and local structured information. Then, our refinement module checks the correspondences further using our proposed novel higher-order filter, which enables the structure conformity of correspondences to improve the quality of inlier correspondences. The final transformation matrix is calculated by using the refined inlier correspondences. Furthermore, a new cross-source point cloud dataset is proposed to further demonstrate the robustness in real-world point clouds. Experimental results demonstrate that our algorithm achieves the state-of-the-art accuracy on both indoor and outdoor, same-source and newly proposed cross-source real-world point clouds. •Works on both same-source and cross-source point cloud registration.•A coarse-to-fine framework combines both merits of deep learning and optimization.•Our algorithm can explicitly remove the outliers.•A real-world cross-source point cloud registration dataset.•Comprehensive experiments on both same-source and cross-source datasets.