{"url":"/dataset/semantic3d","name":"Semantic3D","full_name":null,"description_markdown":"**Semantic3D** is a point cloud dataset of scanned outdoor scenes with over 3 billion points. It contains 15 training and 15 test scenes annotated with 8 class labels. This large labelled 3D point cloud data set of natural covers a range of diverse urban scenes: churches, streets, railroad tracks, squares, villages, soccer fields, castles to name just a few. The point clouds provided are scanned statically with state-of-the-art equipment and contain very fine details.\r\n\r\nSource: [Tangent Convolutions for Dense Prediction in 3D](https://arxiv.org/abs/1807.02443)\r\nImage Source: [http://www.semantic3d.net/](http://www.semantic3d.net/)","description_withheld":null,"homepage":"http://www.semantic3d.net/","introduced_date":"2017-01-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/semantic3dnet-a-new-large-scale-point-cloud","title":"Semantic3D.net: A new Large-scale Point Cloud Classification Benchmark","first_author":"Timo Hackel","url":null},"license":{"name":"CC BY-NC-SA 3.0","url":"https://creativecommons.org/licenses/by-nc-sa/3.0/"},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"3D","url":"/datasets/modality/3d"},{"name":"Point cloud","url":"/datasets/modality/point-cloud"}],"tasks":[{"name":"Semantic Segmentation","url":"/task/semantic-segmentation","datasets_with_task":"/datasets/task/semantic-segmentation"}],"languages":[],"variants":["Semantic3D"],"data_loaders":[{"repo":"https://github.com/QingyongHu/RandLA-Net","url":"https://ethz.ch/content/dam/ethz/special-interest/baug/igp/photogrammetry-remote-sensing-dam/documents/pdf/Papers/Hackel-etal-cmrt2017.pdf","frameworks":[]}],"num_papers_in_archive":66,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/semantic-segmentation-on-semantic3d","task":"Semantic Segmentation","dataset_variant":"Semantic3D","rows":17,"metrics":["mIoU","oAcc"],"first_row_in_archive_order":{"model":"Feature Geometric Net","paper":"/paper/fg-net-fast-large-scale-lidar-point","metrics":{"mIoU":"78.2%","oAcc":"93.6"},"code_links":[{"title":"KangchengLiu/Feature-Geometric-Net-FG-Net","url":"https://github.com/KangchengLiu/Feature-Geometric-Net-FG-Net"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/scf-net-learning-spatial-contextual-features","title":"SCF-Net: Learning Spatial Contextual Features for Large-Scale Point Cloud Segmentation","date":"2021-06-19","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/omni-supervised-point-cloud-segmentation-via","title":"Omni-supervised Point Cloud Segmentation via Gradual Receptive Field Component Reasoning","date":"2021-05-21","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":9,"samples_ran":2,"samples_unverified":7,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/semantic-segmentation-for-real-point-cloud","title":"Semantic Segmentation for Real Point Cloud Scenes via Bilateral Augmentation and Adaptive Fusion","date":"2021-03-12","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/fg-net-fast-large-scale-lidar-point","title":"FG-Net: Fast Large-Scale LiDAR Point Clouds Understanding Network Leveraging Correlated Feature Mining and Geometric-Aware Modelling","date":"2020-12-17","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/191111236","title":"RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds","date":"2019-11-25","rows_on_this_dataset":1,"code_links":9,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":4,"samples_ran":1,"samples_unverified":3,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/shellnet-efficient-point-cloud-convolutional","title":"ShellNet: Efficient Point Cloud Convolutional Neural Networks using Concentric Shells Statistics","date":"2019-08-17","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/graph-attention-convolution-for-point-cloud","title":"Graph Attention Convolution for Point Cloud Semantic Segmentation","date":"2019-06-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/kpconv-flexible-and-deformable-convolution","title":"KPConv: Flexible and Deformable Convolution for Point Clouds","date":"2019-04-18","rows_on_this_dataset":1,"code_links":10,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":12,"samples_ran":5,"samples_unverified":7,"pointer_only_for_licence":3,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/semantic-classification-of-3d-point-clouds","title":"Semantic Classification of 3D Point Clouds with Multiscale Spherical Neighborhoods","date":"2018-08-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/classification-of-point-cloud-scenes-with","title":"Classification of Point Cloud Scenes with Multiscale Voxel Deep Network","date":"2018-04-10","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/large-scale-point-cloud-semantic-segmentation","title":"Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs","date":"2017-11-27","rows_on_this_dataset":2,"code_links":2,"syntology":null},{"paper":"/paper/segcloud-semantic-segmentation-of-3d-point","title":"SEGCloud: Semantic Segmentation of 3D Point Clouds","date":"2017-10-20","rows_on_this_dataset":2,"code_links":0,"syntology":null},{"paper":"/paper/deep-projective-3d-semantic-segmentation","title":"Deep Projective 3D Semantic Segmentation","date":"2017-05-09","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/unstructured-point-cloud-semantic","title":"Unstructured point cloud semantic labelingusing deep segmentation networks","date":"2017-04-23","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/fast-semantic-segmentation-of-3d-point-clouds","title":"Fast semantic segmentation of 3d point clouds with strongly varying density","date":"2016-03-07","rows_on_this_dataset":1,"code_links":0,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":3,"samples_harvested":25,"samples_ran":8,"samples_unverified":17,"pointer_only_for_licence":3,"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."}