{"url":"/sota/3d-instance-segmentation-on-s3dis","task":{"name":"3D Instance Segmentation","url":"/task/3d-instance-segmentation-1","note":null},"dataset":{"name":"S3DIS","url":"/dataset/s3dis"},"category":"Computer Vision","categories":["Computer Vision"],"category_note":null,"description":"Image: [OccuSeg](https://arxiv.org/pdf/2003.06537v3.pdf)","description_from":"task","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","rank":"the archive's row order at snapshot; not re-ranked","rows_end_at":"2025-07-28","rows_withheld_as_spam":0,"metric_values":"the archive's strings, untouched"},"metrics":["AP@50","mAP","mPrec","mRec","mIoU","mAcc","mCov","mWCov"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"AP@50":"higher","mAP":"higher","mPrec":null,"mRec":null,"mIoU":null,"mAcc":null,"mCov":null,"mWCov":null}},"counts":{"rows":21,"rows_with_code":16,"rows_with_paper_page":21,"rows_dated":21,"rows_using_additional_data":4},"rows":[{"rank_in_archive_order":1,"model":"OneFormer3D","metrics":{"AP@50":"75.8","mAP":"63.0","mPrec":"82.3","mRec":"74.1"},"uses_additional_data":false,"paper_date":"2023-11-24","paper":"/paper/oneformer3d-one-transformer-for-unified-point","paper_url":"https://arxiv.org/abs/2311.14405v1","paper_title":"OneFormer3D: One Transformer for Unified Point Cloud Segmentation","code":"https://github.com/oneformer3d/oneformer3d","n_code_links":1,"syntology":null},{"rank_in_archive_order":2,"model":"Mask3D","metrics":{"AP@50":"75.5","mAP":"64.5"},"uses_additional_data":false,"paper_date":"2022-10-06","paper":"/paper/mask3d-for-3d-semantic-instance-segmentation","paper_url":"https://arxiv.org/abs/2210.03105v2","paper_title":"Mask3D: Mask Transformer for 3D Semantic Instance Segmentation","code":"https://github.com/jonasschult/mask3d","n_code_links":1,"syntology":{"n_ran":2,"n_unverified":8,"n_samples":10,"n_pointer_only_licence":0}},{"rank_in_archive_order":3,"model":"PBNet","metrics":{"AP@50":"70.6","mAP":"59.5"},"uses_additional_data":false,"paper_date":"2022-07-22","paper":"/paper/divide-and-conquer-3d-point-cloud-instance","paper_url":"https://arxiv.org/abs/2207.11209v4","paper_title":"Divide and Conquer: 3D Point Cloud Instance Segmentation With Point-Wise Binarization","code":"https://github.com/weiguangzhao/PBNet","n_code_links":1,"syntology":{"n_ran":2,"n_unverified":4,"n_samples":6,"n_pointer_only_licence":0}},{"rank_in_archive_order":4,"model":"ISBNet","metrics":{"AP@50":"70.5","mAP":"60.8","mCov":"74.9","mPrec":"77.5","mRec":"77.1","mWCov":"76.8"},"uses_additional_data":true,"paper_date":"2023-03-01","paper":"/paper/isbnet-a-3d-point-cloud-instance-segmentation","paper_url":"https://arxiv.org/abs/2303.00246v2","paper_title":"ISBNet: a 3D Point Cloud Instance Segmentation Network with Instance-aware Sampling and Box-aware Dynamic Convolution","code":"https://github.com/VinAIResearch/ISBNet","n_code_links":2,"syntology":{"n_ran":2,"n_unverified":17,"n_samples":19,"n_pointer_only_licence":0}},{"rank_in_archive_order":5,"model":"TD3D","metrics":{"AP@50":"70.4","mAP":"58.1"},"uses_additional_data":true,"paper_date":"2023-02-06","paper":"/paper/top-down-beats-bottom-up-in-3d-instance","paper_url":"https://arxiv.org/abs/2302.02871v4","paper_title":"Top-Down Beats Bottom-Up in 3D Instance Segmentation","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":6,"model":"MSTA3D","metrics":{"AP@50":"70.0","mPrec":"80.6","mRec":"70.1"},"uses_additional_data":false,"paper_date":"2024-11-04","paper":"/paper/msta3d-multi-scale-twin-attention-for-3d-1","paper_url":"https://arxiv.org/abs/2411.01781v3","paper_title":"MSTA3D: Multi-scale Twin-attention for 3D Instance Segmentation","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":7,"model":"MaskGroup","metrics":{"AP@50":"69.9","mPrec":"66.6","mRec":"69.6"},"uses_additional_data":false,"paper_date":"2022-03-28","paper":"/paper/maskgroup-hierarchical-point-grouping-and","paper_url":"https://arxiv.org/abs/2203.14662v1","paper_title":"MaskGroup: Hierarchical Point Grouping and Masking for 3D Instance Segmentation","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":8,"model":"SPFormer","metrics":{"AP@50":"69.2","mPrec":"74.0","mRec":"71.1"},"uses_additional_data":false,"paper_date":"2022-11-28","paper":"/paper/superpoint-transformer-for-3d-scene-instance","paper_url":"https://arxiv.org/abs/2211.15766v1","paper_title":"Superpoint Transformer for 3D Scene Instance Segmentation","code":"https://github.com/sunjiahao1999/spformer","n_code_links":1,"syntology":null},{"rank_in_archive_order":9,"model":"SoftGroup","metrics":{"AP@50":"68.9","mAP":"54.4","mCov":"69.3","mPrec":"75.3","mRec":"69.8","mWCov":"71.7"},"uses_additional_data":true,"paper_date":"2022-03-03","paper":"/paper/softgroup-for-3d-instance-segmentation-on","paper_url":"https://arxiv.org/abs/2203.01509v1","paper_title":"SoftGroup for 3D Instance Segmentation on Point Clouds","code":"https://github.com/thangvubk/softgroup","n_code_links":1,"syntology":null},{"rank_in_archive_order":10,"model":"SSTNet","metrics":{"AP@50":"67.8","mAP":"54.1","mPrec":"73.5","mRec":"73.4"},"uses_additional_data":false,"paper_date":"2021-08-17","paper":"/paper/instance-segmentation-in-3d-scenes-using","paper_url":"https://arxiv.org/abs/2108.07478v1","paper_title":"Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networks","code":"https://github.com/gorilla-lab-scut/sstnet","n_code_links":1,"syntology":{"n_ran":0,"n_unverified":8,"n_samples":8,"n_pointer_only_licence":0}},{"rank_in_archive_order":11,"model":"PointGroup","metrics":{"AP@50":"64.0","mPrec":"69.6","mRec":"69.2"},"uses_additional_data":false,"paper_date":"2020-04-03","paper":"/paper/pointgroup-dual-set-point-grouping-for-3d","paper_url":"https://arxiv.org/abs/2004.01658v1","paper_title":"PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation","code":"https://github.com/Pointcept/Pointcept","n_code_links":4,"syntology":{"n_ran":0,"n_unverified":5,"n_samples":5,"n_pointer_only_licence":0}},{"rank_in_archive_order":12,"model":"DKNet","metrics":{"mCov":"70.3","mPrec":"75.3","mRec":"71.1","mWCov":"72.8"},"uses_additional_data":false,"paper_date":"2022-07-15","paper":"/paper/3d-instances-as-1d-kernels","paper_url":"https://arxiv.org/abs/2207.07372v2","paper_title":"3D Instances as 1D Kernels","code":"https://github.com/w1zheng/dknet","n_code_links":1,"syntology":{"n_ran":6,"n_unverified":0,"n_samples":6,"n_pointer_only_licence":0}},{"rank_in_archive_order":13,"model":"HAIS","metrics":{"mCov":"67.0","mPrec":"73.2","mRec":"69.4","mWCov":"70.4"},"uses_additional_data":true,"paper_date":"2021-08-05","paper":"/paper/hierarchical-aggregation-for-3d-instance","paper_url":"https://arxiv.org/abs/2108.02350v1","paper_title":"Hierarchical Aggregation for 3D Instance Segmentation","code":"https://github.com/hustvl/HAIS","n_code_links":1,"syntology":null},{"rank_in_archive_order":14,"model":"GICN","metrics":{"mPrec":"68.5","mRec":"50.8"},"uses_additional_data":false,"paper_date":"2020-07-20","paper":"/paper/learning-gaussian-instance-segmentation-in","paper_url":"https://arxiv.org/abs/2007.09860v1","paper_title":"Learning Gaussian Instance Segmentation in Point Clouds","code":"https://github.com/LiuShihHung/GICN","n_code_links":1,"syntology":{"n_ran":0,"n_unverified":1,"n_samples":1,"n_pointer_only_licence":1}},{"rank_in_archive_order":15,"model":"JSNet","metrics":{"mCov":"54.1","mPrec":"66.9","mRec":"53.9","mWCov":"58"},"uses_additional_data":false,"paper_date":"2019-12-20","paper":"/paper/191209654","paper_url":"https://arxiv.org/abs/1912.09654v1","paper_title":"JSNet: Joint Instance and Semantic Segmentation of 3D Point Clouds","code":"https://github.com/dlinzhao/JSNet","n_code_links":2,"syntology":null},{"rank_in_archive_order":16,"model":"3D-MPA","metrics":{"mPrec":"66.7","mRec":"64.1"},"uses_additional_data":false,"paper_date":"2020-03-30","paper":"/paper/3d-mpa-multi-proposal-aggregation-for-3d","paper_url":"https://arxiv.org/abs/2003.13867v1","paper_title":"3D-MPA: Multi Proposal Aggregation for 3D Semantic Instance Segmentation","code":"https://github.com/francisengelmann/3D-MPA","n_code_links":1,"syntology":null},{"rank_in_archive_order":17,"model":"3D-BoNet","metrics":{"mPrec":"65.6","mRec":"47.6"},"uses_additional_data":false,"paper_date":"2019-06-04","paper":"/paper/learning-object-bounding-boxes-for-3d","paper_url":"https://arxiv.org/abs/1906.01140v2","paper_title":"Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds","code":"https://github.com/Yang7879/3D-BoNet","n_code_links":1,"syntology":{"n_ran":2,"n_unverified":2,"n_samples":4,"n_pointer_only_licence":0}},{"rank_in_archive_order":18,"model":"SASO","metrics":{"mAcc":"72.8","mCov":"54.5","mIoU":"61.1","mPrec":"64.2","mRec":"50.8","mWCov":"58.3"},"uses_additional_data":false,"paper_date":"2020-06-25","paper":"/paper/saso-joint-3d-semantic-instance-segmentation","paper_url":"https://arxiv.org/abs/2006.15015v1","paper_title":"SASO: Joint 3D Semantic-Instance Segmentation via Multi-scale Semantic Association and Salient Point Clustering Optimization","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":19,"model":"ASIS","metrics":{"mPrec":"63.6","mRec":"47.5"},"uses_additional_data":false,"paper_date":"2019-02-26","paper":"/paper/associatively-segmenting-instances-and","paper_url":"http://arxiv.org/abs/1902.09852v2","paper_title":"Associatively Segmenting Instances and Semantics in Point Clouds","code":"https://github.com/WXinlong/ASIS","n_code_links":3,"syntology":null},{"rank_in_archive_order":20,"model":"PartNet","metrics":{"mRec":"43.4%"},"uses_additional_data":false,"paper_date":"2019-03-02","paper":"/paper/partnet-a-recursive-part-decomposition","paper_url":"https://arxiv.org/abs/1903.00709v5","paper_title":"PartNet: A Recursive Part Decomposition Network for Fine-grained and Hierarchical Shape Segmentation","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":21,"model":"PointCNN","metrics":{"mAcc":"75.61","mIoU":"65.39%"},"uses_additional_data":false,"paper_date":"2018-01-23","paper":"/paper/pointcnn-convolution-on-mathcalx-transformed","paper_url":"http://arxiv.org/abs/1801.07791v5","paper_title":"PointCNN: Convolution On $\\mathcal{X}$-Transformed Points","code":"https://github.com/pyg-team/pytorch_geometric","n_code_links":16,"syntology":null}],"since_archive":{"claim":"Results that newer papers report for their own method, placed here by Syntology. A model pointed at the cell in the paper's own table; the number was read from that cell and checked against this leaderboard's metric, dataset, split and scale; an independent check that saw this leaderboard's other rows and every other leaderboard on the same dataset accepted it. Not reviewed by the paper's authors or by the archive's editors, and not ranked against the archive rows.","extraction_file_present":true,"measurement":{"test_papers":883,"papers_with_output":881,"judged_true":108,"judged":110,"wilson95_lower":0.9361,"measured_on":"2026-09-24","frozen_commit":"0e3de0df94"},"measurement_note":"blind adjudication of accepted entries on a held-out split of archive papers, rules frozen before the test","coverage":{"sentence":"Syntology has checked 6,264 of the 9,581 papers on this site that are newer than the archive; results from the others appear after they are checked.","complete":false,"papers_newer_than_archive":9581,"papers_checked":6264,"papers_extracted_not_yet_verified":0,"boards_without_verdict":2,"papers_not_yet_extracted":3316},"order":"newest first by month (arXiv date, else the arXiv-id month), then arXiv id descending","columns":[],"entries":[]},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per row: N of M harvested code samples from that row's paper executed on a synthesized fixture; the other M-N are unverified. Not a reproduction of the row's number; not a correctness claim. n_pointer_only_licence counts samples the site points at rather than redistributes (a licence axis, independent of ran/unverified).","rows_with_graph_line":8,"rows_with_any_sample_ran":5,"distinct_papers_with_graph_line":8,"distinct_papers_with_any_sample_ran":5,"samples_over_distinct_papers":{"n_ran":14,"n_unverified":45,"n_samples":59,"n_pointer_only_licence":1,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":14,"n_unverified":45,"n_samples":59,"n_pointer_only_licence":1,"note":"row-weighted: a paper behind several rows is counted once per row; inflated relative to samples_over_distinct_papers by design, kept for readers summing the per-row syntology blocks"}}}