{"url":"/dataset/s-mid","name":"S.MID","full_name":"SeMantic InDustry","description_markdown":"SeMantic InDustry (S.MID) is a dataset designed to advance the field of LiDAR semantic segmentation, specifically for robotic applications and large-scale industrial scene. The dataset is based on a hybrid-solid LiDAR (Livox Mid-360). To create S.MID, researchers used an industrial robot to collect a total of 38,904 frames of LiDAR data at a rate of 10 Hz across various substations. The LiDAR point clouds are annotated into 25 categories under professional guidance  (14 categories for single frame segmentation task) .","description_withheld":null,"homepage":"https://www.semanticindustry.top","introduced_date":"2024-07-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/sfpnet-sparse-focal-point-network-for","title":"SFPNet: Sparse Focal Point Network for Semantic Segmentation on General LiDAR Point Clouds","first_author":"Yanbo Wang","url":null},"license":{"name":"CC BY-NC-SA 4.0","url":null},"modalities":[{"name":"LiDAR","url":"/datasets/modality/lidar"}],"tasks":[{"name":"LIDAR Semantic Segmentation","url":"/task/lidar-semantic-segmentation","datasets_with_task":"/datasets/task/lidar-semantic-segmentation"}],"languages":[{"name":"English","url":"/datasets/language/english"},{"name":"Chinese","url":"/datasets/language/chinese"}],"variants":["S.MID"],"data_loaders":[{"repo":"https://github.com/Cavendish518/SFPNet","url":"https://github.com/Cavendish518/SFPNet","frameworks":["pytorch"]}],"num_papers_in_archive":5,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/lidar-semantic-segmentation-on-s-mid","task":"LIDAR Semantic Segmentation","dataset_variant":"S.MID","rows":4,"metrics":["val mIoU"],"first_row_in_archive_order":{"model":"SFPNet","paper":"/paper/sfpnet-sparse-focal-point-network-for","metrics":{"val mIoU":"72.8%"},"code_links":[{"title":"Cavendish518/SFPNet","url":"https://github.com/Cavendish518/SFPNet"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/sfpnet-sparse-focal-point-network-for","title":"SFPNet: Sparse Focal Point Network for Semantic Segmentation on General LiDAR Point Clouds","date":"2024-07-16","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":10,"samples_ran":5,"samples_unverified":5,"pointer_only_for_licence":10,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/spherical-transformer-for-lidar-based-3d","title":"Spherical Transformer for LiDAR-based 3D Recognition","date":"2023-03-22","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":13,"samples_ran":4,"samples_unverified":9,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/cylindrical-and-asymmetrical-3d-convolution","title":"Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentation","date":"2020-11-19","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/3d-semantic-segmentation-with-submanifold","title":"3D Semantic Segmentation with Submanifold Sparse Convolutional Networks","date":"2017-11-28","rows_on_this_dataset":1,"code_links":6,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":3,"samples_unverified":0,"pointer_only_for_licence":3,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":3,"samples_harvested":26,"samples_ran":12,"samples_unverified":14,"pointer_only_for_licence":13,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}