{"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/the-h3d-dataset-for-full-surround-3d-multi","title":"The H3D Dataset for Full-Surround 3D Multi-Object Detection and Tracking in Crowded Urban Scenes","arxiv_id":"1903.01568","date":"2019-03-04","proceeding":null,"authors":["Abhishek Patil","Srikanth Malla","Haiming Gang","Yi-Ting Chen"],"abstract":"3D multi-object detection and tracking are crucial for traffic scene\nunderstanding. However, the community pays less attention to these areas due to\nthe lack of a standardized benchmark dataset to advance the field. Moreover,\nexisting datasets (e.g., KITTI) do not provide sufficient data and labels to\ntackle challenging scenes where highly interactive and occluded traffic\nparticipants are present. To address the issues, we present the Honda Research\nInstitute 3D Dataset (H3D), a large-scale full-surround 3D multi-object\ndetection and tracking dataset collected using a 3D LiDAR scanner. H3D\ncomprises of 160 crowded and highly interactive traffic scenes with a total of\n1 million labeled instances in 27,721 frames. With unique dataset size, rich\nannotations, and complex scenes, H3D is gathered to stimulate research on\nfull-surround 3D multi-object detection and tracking. To effectively and\nefficiently annotate a large-scale 3D point cloud dataset, we propose a\nlabeling methodology to speed up the overall annotation cycle. A standardized\nbenchmark is created to evaluate full-surround 3D multi-object detection and\ntracking algorithms. 3D object detection and tracking algorithms are trained\nand tested on H3D. Finally, sources of errors are discussed for the development\nof future algorithms.","url_abs":"http://arxiv.org/abs/1903.01568v1","url_pdf":"http://arxiv.org/pdf/1903.01568v1.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":[],"tasks":[{"task_slug":"3d-object-detection","task_name":"3D Object Detection"},{"task_slug":"object","task_name":"Object"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"scene-understanding","task_name":"Scene Understanding"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[{"method_slug":"speed","method_name":"SPEED"}],"datasets_introduced":[{"slug":"h3d","name":"H3D","full_name":"Honda Research Institute 3D"}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1903.01568","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}