{"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/3d-object-proposals-for-accurate-object-class","title":"3D Object Proposals for Accurate Object Class Detection","arxiv_id":null,"date":"2015-12-01","proceeding":"NeurIPS 2015 12","authors":["Xiaozhi Chen","Kaustav Kundu","Yukun Zhu","Andrew G. Berneshawi","Huimin Ma","Sanja Fidler","Raquel Urtasun"],"abstract":"The goal of this paper is to generate high-quality 3D object proposals in the context of autonomous driving. Our method exploits stereo imagery to  place proposals in the form of 3D bounding boxes. We formulate the problem as minimizing an energy function encoding object size priors,  ground plane as well as several depth informed features that reason about free space, point cloud densities and distance to the ground.  Our experiments  show significant performance gains over existing RGB and RGB-D object proposal methods  on the challenging KITTI benchmark. Combined with convolutional neural net (CNN) scoring, our approach outperforms all existing results on all three KITTI object classes.","url_abs":"http://papers.nips.cc/paper/5644-3d-object-proposals-for-accurate-object-class-detection","url_pdf":"http://papers.nips.cc/paper/5644-3d-object-proposals-for-accurate-object-class-detection.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":"autonomous-driving","task_name":"Autonomous Driving"},{"task_slug":"object","task_name":"Object"},{"task_slug":"vehicle-pose-estimation","task_name":"Vehicle Pose Estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/vehicle-pose-estimation-on-kitti-cars-hard","task":"Vehicle Pose Estimation","dataset":"KITTI Cars Hard","model":"3DOP","rank_in_archive_order":10,"of":19,"metrics":{"Average Orientation Similarity":"76.52"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}