{"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/baeformer-bi-directional-and-early","title":"BAEFormer: Bi-Directional and Early Interaction Transformers for Bird's Eye View Semantic Segmentation","arxiv_id":null,"date":"2023-01-01","proceeding":"CVPR 2023 1","authors":["Cong Pan","Yonghao He","Junran Peng","Qian Zhang","Wei Sui","Zhaoxiang Zhang"],"abstract":"    Bird's Eye View (BEV) semantic segmentation is a critical task in autonomous driving. However, existing Transformer-based methods confront difficulties in transforming Perspective View (PV) to BEV due to their unidirectional and posterior interaction mechanisms. To address this issue, we propose a novel Bi-directional and Early Interaction Transformers framework named BAEFormer, consisting of (i) an early-interaction PV-BEV pipeline and (ii) a bi-directional cross-attention mechanism. Moreover, we find that the image feature maps' resolution in the cross-attention module has a limited effect on the final performance. Under this critical observation, we propose to enlarge the size of input images and downsample the multi-view image features for cross-interaction, further improving the accuracy while keeping the amount of computation controllable. Our proposed method for BEV semantic segmentation achieves state-of-the-art performance in real-time inference speed on the nuScenes dataset, i.e., 38.9 mIoU at 45 FPS on a single A100 GPU.    ","url_abs":"http://openaccess.thecvf.com//content/CVPR2023/html/Pan_BAEFormer_Bi-Directional_and_Early_Interaction_Transformers_for_Birds_Eye_View_CVPR_2023_paper.html","url_pdf":"http://openaccess.thecvf.com//content/CVPR2023/papers/Pan_BAEFormer_Bi-Directional_and_Early_Interaction_Transformers_for_Birds_Eye_View_CVPR_2023_paper.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":"bird-s-eye-view-semantic-segmentation","task_name":"Bird's-Eye View Semantic Segmentation"},{"task_slug":null,"task_name":"GPU"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[{"method_slug":"concatenated-skip-connection","method_name":"Concatenated Skip Connection"},{"method_slug":"speed","method_name":"SPEED"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/bird-s-eye-view-semantic-segmentation-on","task":"Bird's-Eye View Semantic Segmentation","dataset":"nuScenes","model":"BAEFormer","rank_in_archive_order":6,"of":17,"metrics":{"IoU veh - 224x480 - No vis filter - 100x100 at 0.5":"36","IoU veh - 224x480 - Vis filter. - 100x100 at 0.5":"38.9","IoU veh - 448x800 - No vis filter - 100x100 at 0.5":"37.8","IoU veh - 448x800 - Vis filter. - 100x100 at 0.5":"41.0"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}