{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/toronto-3d-a-large-scale-mobile-lidar-dataset","title":"Toronto-3D: A Large-scale Mobile LiDAR Dataset for Semantic Segmentation of Urban Roadways","arxiv_id":"2003.08284","date":"2020-03-18","proceeding":null,"authors":["Weikai Tan","Nannan Qin","Lingfei Ma","Ying Li","Jing Du","Guorong Cai","Ke Yang","Jonathan Li"],"abstract":"Semantic segmentation of large-scale outdoor point clouds is essential for urban scene understanding in various applications, especially autonomous driving and urban high-definition (HD) mapping. With rapid developments of mobile laser scanning (MLS) systems, massive point clouds are available for scene understanding, but publicly accessible large-scale labeled datasets, which are essential for developing learning-based methods, are still limited. This paper introduces Toronto-3D, a large-scale urban outdoor point cloud dataset acquired by a MLS system in Toronto, Canada for semantic segmentation. This dataset covers approximately 1 km of point clouds and consists of about 78.3 million points with 8 labeled object classes. Baseline experiments for semantic segmentation were conducted and the results confirmed the capability of this dataset to train deep learning models effectively. Toronto-3D is released to encourage new research, and the labels will be improved and updated with feedback from the research community.","url_abs":"https://arxiv.org/abs/2003.08284v3","url_pdf":"https://arxiv.org/pdf/2003.08284v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"toronto-3d-a-large-scale-mobile-lidar-dataset","repo_url":"https://github.com/WeikaiTan/Toronto-3D","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"3d-semantic-segmentation","task_name":"3D Semantic Segmentation"},{"task_slug":"autonomous-driving","task_name":"Autonomous Driving"},{"task_slug":"scene-understanding","task_name":"Scene Understanding"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[],"datasets_introduced":[{"slug":"toronto-3d","name":"Toronto-3D","full_name":null}],"methods_introduced":[],"results":[{"leaderboard":"/sota/3d-semantic-segmentation-on-toronto-3d","task":"3D Semantic Segmentation","dataset":"Toronto-3D","model":"KPFCNN","rank_in_archive_order":3,"of":7,"metrics":{"OA":"91.71","mIoU":"60.30"},"uses_additional_data":false},{"leaderboard":"/sota/3d-semantic-segmentation-on-toronto-3d","task":"3D Semantic Segmentation","dataset":"Toronto-3D","model":"TGNet","rank_in_archive_order":4,"of":7,"metrics":{"OA":"91.64","mIoU":"58.34"},"uses_additional_data":false},{"leaderboard":"/sota/3d-semantic-segmentation-on-toronto-3d","task":"3D Semantic Segmentation","dataset":"Toronto-3D","model":"MS-PCNN","rank_in_archive_order":5,"of":7,"metrics":{"OA":"91.53","mIoU":"58.01"},"uses_additional_data":false},{"leaderboard":"/sota/3d-semantic-segmentation-on-toronto-3d","task":"3D Semantic Segmentation","dataset":"Toronto-3D","model":"PointNet++","rank_in_archive_order":6,"of":7,"metrics":{"OA":"91.21","mIoU":"56.55"},"uses_additional_data":false},{"leaderboard":"/sota/3d-semantic-segmentation-on-toronto-3d","task":"3D Semantic Segmentation","dataset":"Toronto-3D","model":"DGCNN","rank_in_archive_order":7,"of":7,"metrics":{"OA":"89.00","mIoU":"49.60"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2003.08284","atlas_url":"https://app.syntology.ai/?focus=2003.08284","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}