{"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/spinnet-learning-a-general-surface-descriptor","title":"SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration","arxiv_id":"2011.12149","date":"2020-11-24","proceeding":"CVPR 2021 1","authors":["Sheng Ao","Qingyong Hu","Bo Yang","Andrew Markham","Yulan Guo"],"abstract":"Extracting robust and general 3D local features is key to downstream tasks such as point cloud registration and reconstruction. Existing learning-based local descriptors are either sensitive to rotation transformations, or rely on classical handcrafted features which are neither general nor representative. In this paper, we introduce a new, yet conceptually simple, neural architecture, termed SpinNet, to extract local features which are rotationally invariant whilst sufficiently informative to enable accurate registration. A Spatial Point Transformer is first introduced to map the input local surface into a carefully designed cylindrical space, enabling end-to-end optimization with SO(2) equivariant representation. A Neural Feature Extractor which leverages the powerful point-based and 3D cylindrical convolutional neural layers is then utilized to derive a compact and representative descriptor for matching. Extensive experiments on both indoor and outdoor datasets demonstrate that SpinNet outperforms existing state-of-the-art techniques by a large margin. More critically, it has the best generalization ability across unseen scenarios with different sensor modalities. The code is available at https://github.com/QingyongHu/SpinNet.","url_abs":"https://arxiv.org/abs/2011.12149v2","url_pdf":"https://arxiv.org/pdf/2011.12149v2.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":"spinnet-learning-a-general-surface-descriptor","repo_url":"https://github.com/QingyongHu/SpinNet","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":"absolute-position-encodings","method_name":"Absolute Position Encodings"},{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"bpe","method_name":"BPE"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"label-smoothing","method_name":"Label Smoothing"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"position-wise-feed-forward-layer","method_name":"Position-Wise Feed-Forward Layer"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"transformer","method_name":"Transformer"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/point-cloud-registration-on-3dmatch-trained","task":"Point Cloud Registration","dataset":"3DMatch (trained on KITTI)","model":"SpinNet","rank_in_archive_order":2,"of":5,"metrics":{"Recall":"0.845"},"uses_additional_data":false},{"leaderboard":"/sota/point-cloud-registration-on-3dmatch-benchmark","task":"Point Cloud Registration","dataset":"3DMatch Benchmark","model":"SpinNet (no code published as of Dec 15 2020)","rank_in_archive_order":4,"of":15,"metrics":{"Feature Matching Recall":"97.6"},"uses_additional_data":false},{"leaderboard":"/sota/point-cloud-registration-on-eth-trained-on","task":"Point Cloud Registration","dataset":"ETH (trained on 3DMatch)","model":"SpinNet","rank_in_archive_order":2,"of":20,"metrics":{"Feature Matching Recall":"0.928","Recall (30cm, 5 degrees)":"73.07"},"uses_additional_data":false},{"leaderboard":"/sota/point-cloud-registration-on-fpv1","task":"Point Cloud Registration","dataset":"FPv1","model":"SpinNet","rank_in_archive_order":5,"of":8,"metrics":{"RRE (degrees)":"3.105","RTE (cm)":"1.670","Recall (3cm, 10 degrees)":"42.46"},"uses_additional_data":false},{"leaderboard":"/sota/point-cloud-registration-on-kitti","task":"Point Cloud Registration","dataset":"KITTI","model":"SpinNet","rank_in_archive_order":3,"of":6,"metrics":{"Success Rate":"99.10"},"uses_additional_data":false},{"leaderboard":"/sota/point-cloud-registration-on-kitti-trained-on","task":"Point Cloud Registration","dataset":"KITTI (trained on 3DMatch)","model":"SpinNet","rank_in_archive_order":9,"of":14,"metrics":{"Success Rate":"81.44"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2011.12149","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2011.12149"}},"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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