{"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/vehicle-detection-from-3d-lidar-using-fully","title":"Vehicle Detection from 3D Lidar Using Fully Convolutional Network","arxiv_id":"1608.07916","date":"2016-08-29","proceeding":null,"authors":["Bo Li","Tianlei Zhang","Tian Xia"],"abstract":"Convolutional network techniques have recently achieved great success in\nvision based detection tasks. This paper introduces the recent development of\nour research on transplanting the fully convolutional network technique to the\ndetection tasks on 3D range scan data. Specifically, the scenario is set as the\nvehicle detection task from the range data of Velodyne 64E lidar. We proposes\nto present the data in a 2D point map and use a single 2D end-to-end fully\nconvolutional network to predict the objectness confidence and the bounding\nboxes simultaneously. By carefully design the bounding box encoding, it is able\nto predict full 3D bounding boxes even using a 2D convolutional network.\nExperiments on the KITTI dataset shows the state-of-the-art performance of the\nproposed method.","url_abs":"http://arxiv.org/abs/1608.07916v1","url_pdf":"http://arxiv.org/pdf/1608.07916v1.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":"object-detection","task_name":"Object Detection"},{"task_slug":"vehicle-detection","task_name":"vehicle detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/object-detection-on-kitti-cars-easy","task":"Object Detection","dataset":"KITTI Cars Easy","model":"VeloFCN","rank_in_archive_order":5,"of":5,"metrics":{"AP":"60.34"},"uses_additional_data":false},{"leaderboard":"/sota/object-detection-on-kitti-cars-hard","task":"Object Detection","dataset":"KITTI Cars Hard","model":"VeloFCN","rank_in_archive_order":5,"of":5,"metrics":{"AP":"42.74"},"uses_additional_data":false},{"leaderboard":"/sota/object-detection-on-kitti-cars-moderate","task":"Object Detection","dataset":"KITTI Cars Moderate","model":"VeloFCN","rank_in_archive_order":4,"of":4,"metrics":{"AP":"47.51"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/1608.07916","atlas_url":"https://app.syntology.ai/?focus=1608.07916","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}