{"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/yolo3d-end-to-end-real-time-3d-oriented","title":"YOLO3D: End-to-end real-time 3D Oriented Object Bounding Box Detection from LiDAR Point Cloud","arxiv_id":"1808.02350","date":"2018-08-07","proceeding":null,"authors":["Waleed Ali","Sherif Abdelkarim","Mohamed Zahran","Mahmoud Zidan","Ahmad El Sallab"],"abstract":"Object detection and classification in 3D is a key task in Automated Driving\n(AD). LiDAR sensors are employed to provide the 3D point cloud reconstruction\nof the surrounding environment, while the task of 3D object bounding box\ndetection in real time remains a strong algorithmic challenge. In this paper,\nwe build on the success of the one-shot regression meta-architecture in the 2D\nperspective image space and extend it to generate oriented 3D object bounding\nboxes from LiDAR point cloud. Our main contribution is in extending the loss\nfunction of YOLO v2 to include the yaw angle, the 3D box center in Cartesian\ncoordinates and the height of the box as a direct regression problem. This\nformulation enables real-time performance, which is essential for automated\ndriving. Our results are showing promising figures on KITTI benchmark,\nachieving real-time performance (40 fps) on Titan X GPU.","url_abs":"http://arxiv.org/abs/1808.02350v1","url_pdf":"http://arxiv.org/pdf/1808.02350v1.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":"yolo3d-end-to-end-real-time-3d-oriented","repo_url":"https://github.com/RichardMinsooGo-ML/Pytorch-Yolo-3d-Yolov3","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"yolo3d-end-to-end-real-time-3d-oriented","repo_url":"https://github.com/RichardMinsooGo-ML/Pytorch-Yolo-3d-Yolov4","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"yolo3d-end-to-end-real-time-3d-oriented","repo_url":"https://github.com/maudzung/YOLO3D-YOLOv4-PyTorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"GPL-3.0"}}],"tasks":[{"task_slug":"3d-point-cloud-reconstruction","task_name":"3D Point Cloud Reconstruction"},{"task_slug":null,"task_name":"GPU"},{"task_slug":"object","task_name":"Object"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"point-cloud-reconstruction","task_name":"Point cloud reconstruction"},{"task_slug":"object-detection-1","task_name":"object-detection"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1808.02350","atlas_url":"https://app.syntology.ai/?focus=1808.02350","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}