{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/pointdsc-robust-point-cloud-registration","title":"PointDSC: Robust Point Cloud Registration using Deep Spatial Consistency","arxiv_id":"2103.05465","date":"2021-03-09","proceeding":"CVPR 2021 1","authors":["Xuyang Bai","Zixin Luo","Lei Zhou","Hongkai Chen","Lei LI","Zeyu Hu","Hongbo Fu","Chiew-Lan Tai"],"abstract":"Removing outlier correspondences is one of the critical steps for successful feature-based point cloud registration. Despite the increasing popularity of introducing deep learning methods in this field, spatial consistency, which is essentially established by a Euclidean transformation between point clouds, has received almost no individual attention in existing learning frameworks. In this paper, we present PointDSC, a novel deep neural network that explicitly incorporates spatial consistency for pruning outlier correspondences. First, we propose a nonlocal feature aggregation module, weighted by both feature and spatial coherence, for feature embedding of the input correspondences. Second, we formulate a differentiable spectral matching module, supervised by pairwise spatial compatibility, to estimate the inlier confidence of each correspondence from the embedded features. With modest computation cost, our method outperforms the state-of-the-art hand-crafted and learning-based outlier rejection approaches on several real-world datasets by a significant margin. We also show its wide applicability by combining PointDSC with different 3D local descriptors.","url_abs":"https://arxiv.org/abs/2103.05465v1","url_pdf":"https://arxiv.org/pdf/2103.05465v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"pointdsc-robust-point-cloud-registration","repo_url":"https://github.com/XuyangBai/PointDSC","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"point-cloud-registration","task_name":"Point Cloud Registration"}],"methods":[{"method_slug":"pruning","method_name":"Pruning"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/point-cloud-registration-on-eth-trained-on","task":"Point Cloud Registration","dataset":"ETH (trained on 3DMatch)","model":"FCGF+PointDSC","rank_in_archive_order":17,"of":20,"metrics":{"Recall (30cm, 5 degrees)":"77.42"},"uses_additional_data":false},{"leaderboard":"/sota/point-cloud-registration-on-eth-trained-on","task":"Point Cloud Registration","dataset":"ETH (trained on 3DMatch)","model":"FPFH+PointDSC","rank_in_archive_order":18,"of":20,"metrics":{"Recall (30cm, 5 degrees)":"41.94"},"uses_additional_data":false},{"leaderboard":"/sota/point-cloud-registration-on-fpv1","task":"Point Cloud Registration","dataset":"FPv1","model":"FCGF + PointDSC","rank_in_archive_order":4,"of":8,"metrics":{"RRE (degrees)":"3.354","RTE (cm)":"1.793","Recall (3cm, 10 degrees)":"47.85"},"uses_additional_data":false},{"leaderboard":"/sota/point-cloud-registration-on-kitti-trained-on","task":"Point Cloud Registration","dataset":"KITTI (trained on 3DMatch)","model":"FCGF+PointDSC","rank_in_archive_order":3,"of":14,"metrics":{"Success Rate":"96.76"},"uses_additional_data":false},{"leaderboard":"/sota/point-cloud-registration-on-kitti-trained-on","task":"Point Cloud Registration","dataset":"KITTI (trained on 3DMatch)","model":"FPFH+PointDSC","rank_in_archive_order":5,"of":14,"metrics":{"Success Rate":"94.05"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2103.05465","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.05465"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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