| Residential College | false |
| Status | 即將出版Forthcoming |
| GraphBEV: Towards Robust BEV Feature Alignment for Multi-modal 3D Object Detection | |
| Song, Ziying1,2; Yang, Lei3; Xu, Shaoqing4; Liu, Lin1,2; Xu, Dongyang3; Jia, Caiyan1,2; Jia, Feiyang1,2; Wang, Li5 | |
| 2025 | |
| Source Publication | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
 |
| Volume | 15084 LNCS |
| Pages | 347-366 |
| Abstract | Integrating LiDAR and camera information into Bird’s-Eye-View (BEV) representation has emerged as a crucial aspect of 3D object detection in autonomous driving. However, existing methods are susceptible to the inaccurate calibration relationship between LiDAR and the camera sensor. Such inaccuracies result in errors in depth estimation for the camera branch, ultimately causing misalignment between LiDAR and camera BEV features. In this work, we propose a robust fusion framework called GraphBEV. Addressing errors caused by inaccurate point cloud projection, we introduce a LocalAlign module that employs neighbor-aware depth features via Graph matching. Additionally, we propose a GlobalAlign module to rectify the misalignment between LiDAR and camera BEV features. Our GraphBEV framework achieves state-of-the-art performance, with an mAP of 70.1%, surpassing BEVFusion by 1.6% on the nuScnes validation set. Importantly, our GraphBEV outperforms BEVFusion by 8.3% under conditions with misalignment noise. |
| Keyword | 3D Object Detection Bird’s-Eye-View (BEV) Representation Feature Alignment Multi-Modal Fusion |
| DOI | 10.1007/978-3-031-73347-5_20 |
| URL | View the original |
| Language | 英語English |
| Scopus ID | 2-s2.0-85209796993 |
| Fulltext Access | |
| Citation statistics | |
| Document Type | Conference paper |
| Collection | University of Macau |
| Affiliation | 1.School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China 2.Beijing Key Lab of Traffic Data Analysis and Mining, Beijing, China 3.School of Vehicle and Mobility, Tsinghua University, Beijing, China 4.Department of Electrome chanical Engineering, University of Macau, Zhuhai, China 5.School of Mechanical Engineering, Beijing Institute of Technology, Beijing, China |
| Recommended Citation GB/T 7714 | Song, Ziying,Yang, Lei,Xu, Shaoqing,et al. GraphBEV: Towards Robust BEV Feature Alignment for Multi-modal 3D Object Detection[C], 2025, 347-366. |
| APA | Song, Ziying., Yang, Lei., Xu, Shaoqing., Liu, Lin., Xu, Dongyang., Jia, Caiyan., Jia, Feiyang., & Wang, Li (2025). GraphBEV: Towards Robust BEV Feature Alignment for Multi-modal 3D Object Detection. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 15084 LNCS, 347-366. |
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