{"url":"/dataset/toronto-3d","name":"Toronto-3D","full_name":null,"description_markdown":"**Toronto-3D** is a large-scale urban outdoor point cloud dataset acquired by an MLS system in Toronto, Canada for semantic segmentation. This dataset covers approximately 1 km of road and consists of about 78.3 million points. Point clouds has 10 attributes and classified in 8 labelled object classes.\n\nSource: [https://github.com/WeikaiTan/Toronto-3D](https://github.com/WeikaiTan/Toronto-3D)\nImage Source: [https://github.com/WeikaiTan/Toronto-3D](https://github.com/WeikaiTan/Toronto-3D)","description_withheld":null,"homepage":"https://github.com/WeikaiTan/Toronto-3D","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":"/paper/toronto-3d-a-large-scale-mobile-lidar-dataset","title":"Toronto-3D: A Large-scale Mobile LiDAR Dataset for Semantic Segmentation of Urban Roadways","first_author":"Weikai Tan","url":null},"license":null,"modalities":[{"name":"Point cloud","url":"/datasets/modality/point-cloud"}],"tasks":[{"name":"Semantic Segmentation","url":"/task/semantic-segmentation","datasets_with_task":"/datasets/task/semantic-segmentation"},{"name":"Scene Understanding","url":"/task/scene-understanding","datasets_with_task":"/datasets/task/scene-understanding"},{"name":"3D Semantic Segmentation","url":"/task/3d-semantic-segmentation","datasets_with_task":"/datasets/task/3d-semantic-segmentation"},{"name":"Autonomous Driving","url":"/task/autonomous-driving","datasets_with_task":"/datasets/task/autonomous-driving"}],"languages":[],"variants":["Toronto-3D","Toronto-3D L002"],"data_loaders":[{"repo":"https://github.com/WeikaiTan/Toronto-3D","url":"https://github.com/WeikaiTan/Toronto-3D","frameworks":["tf"]}],"num_papers_in_archive":24,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/3d-semantic-segmentation-on-toronto-3d","task":"3D Semantic Segmentation","dataset_variant":"Toronto-3D","rows":7,"metrics":["OA","mIoU"],"first_row_in_archive_order":{"model":"SCF-Net","paper":"/paper/scf-net-learning-spatial-contextual-features","metrics":{"OA":"95.50","mIoU":"73.60"},"code_links":[{"title":"leofansq/SCF-Net","url":"https://github.com/leofansq/SCF-Net"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/semantic-segmentation-on-toronto-3d-l002","task":"Semantic Segmentation","dataset_variant":"Toronto-3D L002","rows":5,"metrics":["oAcc","mIoU"],"first_row_in_archive_order":{"model":"EyeNet","paper":"/paper/mrnet-multiple-input-receptive-field-network","metrics":{"mIoU":"81.13","oAcc":"94.63"},"code_links":[{"title":"Yacovitch/EyeNet","url":"https://github.com/Yacovitch/EyeNet"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/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","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/mrnet-multiple-input-receptive-field-network","title":"Human Vision Based 3D Point Cloud Semantic Segmentation of Large-Scale Outdoor Scene","date":"2023-01-30","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"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/toronto-3d-a-large-scale-mobile-lidar-dataset","title":"Toronto-3D: A Large-scale Mobile LiDAR Dataset for Semantic Segmentation of Urban Roadways","date":"2020-03-18","rows_on_this_dataset":5,"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":2,"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/pointnet-deep-hierarchical-feature-learning","title":"PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space","date":"2017-06-07","rows_on_this_dataset":1,"code_links":68,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":67,"samples_ran":36,"samples_unverified":31,"pointer_only_for_licence":26,"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":2,"samples_harvested":71,"samples_ran":37,"samples_unverified":34,"pointer_only_for_licence":26,"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."}