{"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/fb-occ-3d-occupancy-prediction-based-on","title":"FB-OCC: 3D Occupancy Prediction based on Forward-Backward View Transformation","arxiv_id":"2307.01492","date":"2023-07-04","proceeding":null,"authors":["Zhiqi Li","Zhiding Yu","David Austin","Mingsheng Fang","Shiyi Lan","Jan Kautz","Jose M. Alvarez"],"abstract":"This technical report summarizes the winning solution for the 3D Occupancy Prediction Challenge, which is held in conjunction with the CVPR 2023 Workshop on End-to-End Autonomous Driving and CVPR 23 Workshop on Vision-Centric Autonomous Driving Workshop. Our proposed solution FB-OCC builds upon FB-BEV, a cutting-edge camera-based bird's-eye view perception design using forward-backward projection. On top of FB-BEV, we further study novel designs and optimization tailored to the 3D occupancy prediction task, including joint depth-semantic pre-training, joint voxel-BEV representation, model scaling up, and effective post-processing strategies. These designs and optimization result in a state-of-the-art mIoU score of 54.19% on the nuScenes dataset, ranking the 1st place in the challenge track. Code and models will be released at: https://github.com/NVlabs/FB-BEV.","url_abs":"https://arxiv.org/abs/2307.01492v1","url_pdf":"https://arxiv.org/pdf/2307.01492v1.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":"fb-occ-3d-occupancy-prediction-based-on","repo_url":"https://github.com/nvlabs/fb-bev","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"autonomous-driving","task_name":"Autonomous Driving"},{"task_slug":"prediction-of-occupancy-grid-maps","task_name":"Prediction Of Occupancy Grid Maps"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/prediction-of-occupancy-grid-maps-on-occ3d","task":"Prediction Of Occupancy Grid Maps","dataset":"Occ3D-nuScenes","model":"FB-OCC-K","rank_in_archive_order":2,"of":8,"metrics":{"mIoU":"52.79"},"uses_additional_data":false},{"leaderboard":"/sota/prediction-of-occupancy-grid-maps-on-occ3d","task":"Prediction Of Occupancy Grid Maps","dataset":"Occ3D-nuScenes","model":"FB-OCC-H","rank_in_archive_order":6,"of":8,"metrics":{"mIoU":"42.06"},"uses_additional_data":false},{"leaderboard":"/sota/prediction-of-occupancy-grid-maps-on-occ3d","task":"Prediction Of Occupancy Grid Maps","dataset":"Occ3D-nuScenes","model":"FB-OCC-G","rank_in_archive_order":7,"of":8,"metrics":{"mIoU":"40.69"},"uses_additional_data":false},{"leaderboard":"/sota/prediction-of-occupancy-grid-maps-on-occ3d","task":"Prediction Of Occupancy Grid Maps","dataset":"Occ3D-nuScenes","model":"CTF-Occ","rank_in_archive_order":8,"of":8,"metrics":{"mIoU":"28.53"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2307.01492","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}