{"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/bevformer-learning-bird-s-eye-view","title":"BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers","arxiv_id":"2203.17270","date":"2022-03-31","proceeding":null,"authors":["Zhiqi Li","Wenhai Wang","Hongyang Li","Enze Xie","Chonghao Sima","Tong Lu","Qiao Yu","Jifeng Dai"],"abstract":"3D visual perception tasks, including 3D detection and map segmentation based on multi-camera images, are essential for autonomous driving systems. In this work, we present a new framework termed BEVFormer, which learns unified BEV representations with spatiotemporal transformers to support multiple autonomous driving perception tasks. In a nutshell, BEVFormer exploits both spatial and temporal information by interacting with spatial and temporal space through predefined grid-shaped BEV queries. To aggregate spatial information, we design spatial cross-attention that each BEV query extracts the spatial features from the regions of interest across camera views. For temporal information, we propose temporal self-attention to recurrently fuse the history BEV information. Our approach achieves the new state-of-the-art 56.9\\% in terms of NDS metric on the nuScenes \\texttt{test} set, which is 9.0 points higher than previous best arts and on par with the performance of LiDAR-based baselines. We further show that BEVFormer remarkably improves the accuracy of velocity estimation and recall of objects under low visibility conditions. The code is available at \\url{https://github.com/zhiqi-li/BEVFormer}.","url_abs":"https://arxiv.org/abs/2203.17270v2","url_pdf":"https://arxiv.org/pdf/2203.17270v2.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":"bevformer-learning-bird-s-eye-view","repo_url":"https://github.com/zhiqi-li/BEVFormer","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"bevformer-learning-bird-s-eye-view","repo_url":"https://github.com/fundamentalvision/BEVFormer","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"bevformer-learning-bird-s-eye-view","repo_url":"https://github.com/valeoai/pointbev","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"3d-object-detection","task_name":"3D Object Detection"},{"task_slug":"autonomous-driving","task_name":"Autonomous Driving"},{"task_slug":"bird-s-eye-view-semantic-segmentation","task_name":"Bird's-Eye View Semantic Segmentation"},{"task_slug":"robust-camera-only-3d-object-detection","task_name":"Robust Camera Only 3D Object Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/3d-object-detection-on-dair-v2x-i","task":"3D Object Detection","dataset":"DAIR-V2X-I","model":"BEVFormer","rank_in_archive_order":8,"of":9,"metrics":{"AP|R40(easy)":"61.4","AP|R40(hard)":"50.7","AP|R40(moderate)":"50.7"},"uses_additional_data":false},{"leaderboard":"/sota/3d-object-detection-on-nuscenes","task":"3D Object Detection","dataset":"nuScenes","model":"BEVFormer","rank_in_archive_order":222,"of":372,"metrics":{"NDS":"0.57","mAAE":"0.13","mAOE":"0.38","mAP":"0.48","mASE":"0.26","mATE":"0.58","mAVE":"0.38"},"uses_additional_data":false},{"leaderboard":"/sota/3d-object-detection-on-nuscenes-camera-only","task":"3D Object Detection","dataset":"nuScenes Camera Only","model":"BEVFormer","rank_in_archive_order":19,"of":19,"metrics":{"Future Frame":"false","NDS":"56.9"},"uses_additional_data":false},{"leaderboard":"/sota/bird-s-eye-view-semantic-segmentation-on-lyft","task":"Bird's-Eye View Semantic Segmentation","dataset":"Lyft Level 5","model":"BEVFormer (EfficientNet-b4)","rank_in_archive_order":4,"of":7,"metrics":{"IoU vehicle - 224x480 - Long":"44.5","IoU vehicle - 224x480 - Short":"69.9"},"uses_additional_data":false},{"leaderboard":"/sota/bird-s-eye-view-semantic-segmentation-on-lyft","task":"Bird's-Eye View Semantic Segmentation","dataset":"Lyft Level 5","model":"BEVFormer(ResNet-50)","rank_in_archive_order":6,"of":7,"metrics":{"IoU vehicle - 224x480 - Long":"43.2","IoU vehicle - 224x480 - Short":"68.8"},"uses_additional_data":false},{"leaderboard":"/sota/bird-s-eye-view-semantic-segmentation-on","task":"Bird's-Eye View Semantic Segmentation","dataset":"nuScenes","model":"BEVFormer","rank_in_archive_order":4,"of":17,"metrics":{"IoU lane - 224x480 - 100x100 at 0.5":"25.7","IoU veh - 224x480 - No vis filter - 100x100 at 0.5":"35.8","IoU veh - 224x480 - Vis filter. - 100x100 at 0.5":"42.0","IoU veh - 448x800 - No vis filter - 100x100 at 0.5":"39.0","IoU veh - 448x800 - Vis filter. - 100x100 at 0.5":"45.5"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2203.17270","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}