{"url":"/task/lidar-semantic-segmentation","name":"LIDAR Semantic Segmentation","slug":"lidar-semantic-segmentation","description_markdown":null,"categories":[{"name":"Computer Vision","url":"/area/computer-vision"}],"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","slug_source":"archive_url"},"counts":{"papers_tagged":129,"papers_with_code":74,"benchmarks":6,"benchmark_tables_in_archive":6,"benchmark_tables_shown":6,"benchmark_tables_withheld_as_spam":0,"benchmark_definition":"a leaderboard table with at least one row; benchmark_tables_shown also counts the zero-row tables; benchmark_tables_in_archive adds the tables withheld as spam","datasets":10,"subtasks":0,"parent_tasks":0},"benchmarks":[{"leaderboard":"/sota/lidar-semantic-segmentation-on-nuscenes","slug":"lidar-semantic-segmentation-on-nuscenes","dataset":"nuScenes","dataset_url":"/dataset/nuscenes","rows_in_archive":36,"metrics":["test mIoU","val mIoU"],"first_row_in_archive_order":{"model":"DITR","paper_title":"DINO in the Room: Leveraging 2D Foundation Models for 3D Segmentation","paper_url":"/paper/dino-in-the-room-leveraging-2d-foundation","paper_date":"2025-03-24","arxiv_id":"2503.18944","code_links":[{"title":"VisualComputingInstitute/DITR","url":"https://github.com/VisualComputingInstitute/DITR"}],"syntology":null}},{"leaderboard":"/sota/lidar-semantic-segmentation-on-paris-lille-3d","slug":"lidar-semantic-segmentation-on-paris-lille-3d","dataset":"Paris-Lille-3D","dataset_url":"/dataset/paris-lille-3d","rows_in_archive":9,"metrics":["mIOU"],"first_row_in_archive_order":{"model":"FKAConv","paper_title":"FKAConv: Feature-Kernel Alignment for Point Cloud Convolution","paper_url":"/paper/lightconvpoint-convolution-for-points","paper_date":"2020-04-09","arxiv_id":"2004.04462","code_links":[{"title":"valeoai/FKAConv","url":"https://github.com/valeoai/FKAConv"}],"syntology":null}},{"leaderboard":"/sota/lidar-semantic-segmentation-on-s-mid","slug":"lidar-semantic-segmentation-on-s-mid","dataset":"S.MID","dataset_url":"/dataset/s-mid","rows_in_archive":4,"metrics":["val mIoU"],"first_row_in_archive_order":{"model":"SFPNet","paper_title":"SFPNet: Sparse Focal Point Network for Semantic Segmentation on General LiDAR Point Clouds","paper_url":"/paper/sfpnet-sparse-focal-point-network-for","paper_date":"2024-07-16","arxiv_id":"2407.11569","code_links":[{"title":"Cavendish518/SFPNet","url":"https://github.com/Cavendish518/SFPNet"}],"syntology":{"n":10,"n_ran":5,"n_unverified":5,"n_pointer_only":10}}},{"leaderboard":"/sota/lidar-semantic-segmentation-on-semantickitti","slug":"lidar-semantic-segmentation-on-semantickitti","dataset":"SemanticKITTI","dataset_url":"/dataset/semantickitti","rows_in_archive":4,"metrics":["mIOU","val mIoU"],"first_row_in_archive_order":{"model":"AF2S3Net","paper_title":null,"paper_url":null,"paper_date":"","arxiv_id":null,"code_links":[],"syntology":null}},{"leaderboard":"/sota/lidar-semantic-segmentation-on-semanticstf","slug":"lidar-semantic-segmentation-on-semanticstf","dataset":"SemanticSTF","dataset_url":"/dataset/semanticstf","rows_in_archive":2,"metrics":["Mean IoU"],"first_row_in_archive_order":{"model":"SJ+LPD (SemanticKITTI2SemanticSTF)","paper_title":"Rethinking Data Augmentation for Robust LiDAR Semantic Segmentation in Adverse Weather","paper_url":"/paper/rethinking-data-augmentation-for-robust-lidar","paper_date":"2024-07-02","arxiv_id":"2407.02286","code_links":[{"title":"engineerjpark/lidarweather","url":"https://github.com/engineerjpark/lidarweather"},{"title":"engineerJPark/LiDAR-DataAug4Weather","url":"https://github.com/engineerJPark/LiDAR-DataAug4Weather"}],"syntology":null}},{"leaderboard":"/sota/lidar-semantic-segmentation-on-uls-labeled","slug":"lidar-semantic-segmentation-on-uls-labeled","dataset":"ULS labeled data","dataset_url":"/dataset/uls-labeled-data","rows_in_archive":1,"metrics":["Binary Accuracy","G-mean","Specificity"],"first_row_in_archive_order":{"model":"SOUL","paper_title":"Semantic segmentation of sparse irregular point clouds for leaf/wood discrimination","paper_url":"/paper/semantic-segmentation-of-sparse-irregular-1","paper_date":"2023-05-26","arxiv_id":"2305.16963","code_links":[{"title":"na1an/phd_mission","url":"https://github.com/na1an/phd_mission"}],"syntology":{"n":3,"n_ran":2,"n_unverified":1,"n_pointer_only":3}}}],"datasets":[{"url":"/dataset/nuscenes","name":"nuScenes","full_name":"","num_papers_in_archive":2139},{"url":"/dataset/semantickitti","name":"SemanticKITTI","full_name":"","num_papers_in_archive":669},{"url":"/dataset/scribblekitti","name":"ScribbleKITTI","full_name":"","num_papers_in_archive":19},{"url":"/dataset/paris-lille-3d","name":"Paris-Lille-3D","full_name":"","num_papers_in_archive":15},{"url":"/dataset/semanticstf","name":"SemanticSTF","full_name":"","num_papers_in_archive":12},{"url":"/dataset/wildscenes","name":"WildScenes","full_name":"","num_papers_in_archive":12},{"url":"/dataset/s-mid","name":"S.MID","full_name":"SeMantic InDustry","num_papers_in_archive":5},{"url":"/dataset/a-curb-dataset","name":"A Curb Dataset","full_name":"","num_papers_in_archive":1},{"url":"/dataset/nuscenes-cross-city-uda","name":"nuScenes (Cross-City UDA)","full_name":"","num_papers_in_archive":1},{"url":"/dataset/uls-labeled-data","name":"ULS labeled data","full_name":"UVA laser scanning labelled las data over tropical moist forest classified as leaf or wood points","num_papers_in_archive":1}],"subtasks":[],"parent_tasks":[],"papers":{"order":"repositories listed in the archive (desc), then date (desc); the archive holds no stars","population":"papers tagged with this task that list at least one repository in the archive","shown":30,"of":74,"tagged_in_all":129,"items":[{"url":"/paper/attention-is-all-you-need","title":"Attention Is All You Need","date":"2017-06-12","arxiv_id":"1706.03762","repositories_listed":595,"syntology":{"n":946,"n_ran":600,"n_unverified":346,"n_pointer_only":451}},{"url":"/paper/ssd-single-shot-multibox-detector","title":"SSD: Single Shot MultiBox Detector","date":"2015-12-08","arxiv_id":"1512.02325","repositories_listed":221,"syntology":{"n":131,"n_ran":19,"n_unverified":112,"n_pointer_only":5}},{"url":"/paper/kpconv-flexible-and-deformable-convolution","title":"KPConv: Flexible and Deformable Convolution for Point Clouds","date":"2019-04-18","arxiv_id":"1904.08889","repositories_listed":10,"syntology":{"n":12,"n_ran":5,"n_unverified":7,"n_pointer_only":3}},{"url":"/paper/191111236","title":"RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds","date":"2019-11-25","arxiv_id":"1911.11236","repositories_listed":9,"syntology":{"n":4,"n_ran":1,"n_unverified":3,"n_pointer_only":0}},{"url":"/paper/searching-efficient-3d-architectures-with","title":"Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution","date":"2020-07-31","arxiv_id":"2007.16100","repositories_listed":6,"syntology":{"n":3,"n_ran":0,"n_unverified":3,"n_pointer_only":0}},{"url":"/paper/3d-semantic-segmentation-with-submanifold","title":"3D Semantic Segmentation with Submanifold Sparse Convolutional Networks","date":"2017-11-28","arxiv_id":"1711.10275","repositories_listed":6,"syntology":{"n":3,"n_ran":3,"n_unverified":0,"n_pointer_only":3}},{"url":"/paper/lenet-lightweight-and-efficient-lidar","title":"LENet: Lightweight And Efficient LiDAR Semantic Segmentation Using Multi-Scale Convolution Attention","date":"2023-01-11","arxiv_id":"2301.04275","repositories_listed":4,"syntology":null},{"url":"/paper/polarnet-an-improved-grid-representation-for","title":"PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation","date":"2020-03-31","arxiv_id":"2003.14032","repositories_listed":4,"syntology":{"n":11,"n_ran":9,"n_unverified":2,"n_pointer_only":1}},{"url":"/paper/point-transformer-v3-simpler-faster-stronger","title":"Point Transformer V3: Simpler, Faster, Stronger","date":"2023-12-15","arxiv_id":"2312.10035","repositories_listed":3,"syntology":{"n":11,"n_ran":7,"n_unverified":4,"n_pointer_only":0}},{"url":"/paper/cenet-toward-concise-and-efficient-lidar","title":"CENet: Toward Concise and Efficient LiDAR Semantic Segmentation for Autonomous Driving","date":"2022-07-26","arxiv_id":"2207.12691","repositories_listed":3,"syntology":{"n":1,"n_ran":0,"n_unverified":1,"n_pointer_only":0}},{"url":"/paper/cpgnet-cascade-point-grid-fusion-network-for","title":"CPGNet: Cascade Point-Grid Fusion Network for Real-Time LiDAR Semantic Segmentation","date":"2022-04-21","arxiv_id":"2204.09914","repositories_listed":3,"syntology":{"n":4,"n_ran":1,"n_unverified":3,"n_pointer_only":0}},{"url":"/paper/scribble-supervised-lidar-semantic","title":"Scribble-Supervised LiDAR Semantic Segmentation","date":"2022-03-16","arxiv_id":"2203.08537","repositories_listed":3,"syntology":null},{"url":"/paper/cylinder3d-an-effective-3d-framework-for","title":"Cylinder3D: An Effective 3D Framework for Driving-scene LiDAR Semantic Segmentation","date":"2020-08-04","arxiv_id":"2008.01550","repositories_listed":3,"syntology":null},{"url":"/paper/rethinking-data-augmentation-for-robust-lidar","title":"Rethinking Data Augmentation for Robust LiDAR Semantic Segmentation in Adverse Weather","date":"2024-07-02","arxiv_id":"2407.02286","repositories_listed":2,"syntology":null},{"url":"/paper/frnet-frustum-range-networks-for-scalable","title":"FRNet: Frustum-Range Networks for Scalable LiDAR Segmentation","date":"2023-12-07","arxiv_id":"2312.04484","repositories_listed":2,"syntology":{"n":19,"n_ran":14,"n_unverified":5,"n_pointer_only":18}},{"url":"/paper/spherical-transformer-for-lidar-based-3d","title":"Spherical Transformer for LiDAR-based 3D Recognition","date":"2023-03-22","arxiv_id":"2303.12766","repositories_listed":2,"syntology":{"n":13,"n_ran":4,"n_unverified":9,"n_pointer_only":0}},{"url":"/paper/point-transformer-v2-grouped-vector-attention","title":"Point Transformer V2: Grouped Vector Attention and Partition-based Pooling","date":"2022-10-11","arxiv_id":"2210.05666","repositories_listed":2,"syntology":null},{"url":"/paper/polarmix-a-general-data-augmentation","title":"PolarMix: A General Data Augmentation Technique for LiDAR Point Clouds","date":"2022-07-30","arxiv_id":"2208.00223","repositories_listed":2,"syntology":{"n":10,"n_ran":3,"n_unverified":7,"n_pointer_only":0}},{"url":"/paper/cosmix-compositional-semantic-mix-for-domain","title":"CoSMix: Compositional Semantic Mix for Domain Adaptation in 3D LiDAR Segmentation","date":"2022-07-20","arxiv_id":"2207.09778","repositories_listed":2,"syntology":null},{"url":"/paper/lasermix-for-semi-supervised-lidar-semantic","title":"LaserMix for Semi-Supervised LiDAR Semantic Segmentation","date":"2022-06-30","arxiv_id":"2207.00026","repositories_listed":2,"syntology":null},{"url":"/paper/cylindrical-and-asymmetrical-3d-convolution","title":"Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentation","date":"2020-11-19","arxiv_id":"2011.10033","repositories_listed":2,"syntology":null},{"url":"/paper/rangenet-fast-and-accurate-lidar-semantic","title":"RangeNet++: Fast and Accurate LiDAR Semantic Segmentation","date":"2019-11-04","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/beyond-one-shot-beyond-one-perspective-cross","title":"Beyond One Shot, Beyond One Perspective: Cross-View and Long-Horizon Distillation for Better LiDAR Representations","date":"2025-07-07","arxiv_id":"2507.05260","repositories_listed":1,"syntology":null},{"url":"/paper/nuc-net-non-uniform-cylindrical-partition","title":"NUC-Net: Non-uniform Cylindrical Partition Network for Efficient LiDAR Semantic Segmentation","date":"2025-05-30","arxiv_id":"2505.24634","repositories_listed":1,"syntology":null},{"url":"/paper/dino-in-the-room-leveraging-2d-foundation","title":"DINO in the Room: Leveraging 2D Foundation Models for 3D Segmentation","date":"2025-03-24","arxiv_id":"2503.18944","repositories_listed":1,"syntology":null},{"url":"/paper/synthmanticlidar-a-synthetic-dataset-for","title":"SynthmanticLiDAR: A Synthetic Dataset for Semantic Segmentation on LiDAR Imaging","date":"2025-01-31","arxiv_id":"2501.19035","repositories_listed":1,"syntology":null},{"url":"/paper/3dlabelprop-geometric-driven-domain","title":"3DLabelProp: Geometric-Driven Domain Generalization for LiDAR Semantic Segmentation in Autonomous Driving","date":"2025-01-24","arxiv_id":"2501.14605","repositories_listed":1,"syntology":null},{"url":"/paper/pc-bev-an-efficient-polar-cartesian-bev","title":"PC-BEV: An Efficient Polar-Cartesian BEV Fusion Framework for LiDAR Semantic Segmentation","date":"2024-12-19","arxiv_id":"2412.14821","repositories_listed":1,"syntology":null},{"url":"/paper/cloudspam-contrastive-learning-on-unlabeled","title":"CLOUDSPAM: Contrastive Learning On Unlabeled Data for Segmentation and Pre-Training Using Aggregated Point Clouds and MoCo","date":"2024-10-26","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/learning-from-spatio-temporal-correlation-for","title":"Learning from Spatio-temporal Correlation for Semi-Supervised LiDAR Semantic Segmentation","date":"2024-10-09","arxiv_id":"2410.06893","repositories_listed":1,"syntology":null}],"syntology_records":13,"syntology_note":"a paper without a record is not a recorded non-run: it may lack an arXiv id or simply be absent from the graph layer"},"description_links":{"kept":0,"unwrapped_to_text":0,"bare_urls_linked":0,"relative_images_dropped":0,"rule":"internal links are kept only when the target slug exists in the catalog"},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per-sample execution status on synthesized fixtures ('ran N of M samples'); not a correctness claim and not a ranking signal.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"}},"not_shown":{"libraries":"the archive has no per-task library table","trend_sparklines":"the Trend column of the benchmarks table was a rendered image; it is not in the archive","social_and_latest_sorts":"stars and social signals are not in the archive"}}