{"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/learning-better-representations-for-crowded","title":"Learning better representations for crowded pedestrians in offboard LiDAR-camera 3D tracking-by-detection","arxiv_id":"2505.16029","date":"2025-05-21","proceeding":null,"authors":["Shichao Li","Peiliang Li","Qing Lian","Peng Yun","Xiaozhi Chen"],"abstract":"Perceiving pedestrians in highly crowded urban environments is a difficult long-tail problem for learning-based autonomous perception. Speeding up 3D ground truth generation for such challenging scenes is performance-critical yet very challenging. The difficulties include the sparsity of the captured pedestrian point cloud and a lack of suitable benchmarks for a specific system design study. To tackle the challenges, we first collect a new multi-view LiDAR-camera 3D multiple-object-tracking benchmark of highly crowded pedestrians for in-depth analysis. We then build an offboard auto-labeling system that reconstructs pedestrian trajectories from LiDAR point cloud and multi-view images. To improve the generalization power for crowded scenes and the performance for small objects, we propose to learn high-resolution representations that are density-aware and relationship-aware. Extensive experiments validate that our approach significantly improves the 3D pedestrian tracking performance towards higher auto-labeling efficiency. The code will be publicly available at this HTTP URL.","url_abs":"https://arxiv.org/abs/2505.16029v1","url_pdf":"https://arxiv.org/pdf/2505.16029v1.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":"learning-better-representations-for-crowded","repo_url":"https://github.com/nicholasli1995/pcp-mv","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"3d-pedestrian-tracking","task_name":"3D Pedestrian Tracking"},{"task_slug":"multiple-object-tracking","task_name":"Multiple Object Tracking"},{"task_slug":"object-tracking","task_name":"Object Tracking"}],"methods":[{"method_slug":"url","method_name":"URL"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/3d-pedestrian-tracking-on-nuscenes-validation","task":"3D Pedestrian Tracking","dataset":"nuScenes validation set","model":"BEVFusion + Tracking","rank_in_archive_order":1,"of":4,"metrics":{"AMOTA":"77.9"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}