Fast-BEV: A Fast and Strong Bird's-Eye View Perception Baseline

• Published in: IEEE transactions on pattern analysis and machine intelligence 2024-12, Vol.46 (12), p.8665-8679
• Main Authors: Li, Yangguang, Huang, Bin, Chen, Zeren, Cui, Yufeng, Liang, Feng, Shen, Mingzhu, Liu, Fenggang, Xie, Enze, Sheng, Lu, Ouyang, Wanli, Shao, Jing
• Format: Article
• Language: English
• Subjects: autonomous driving; Benchmark testing; bird’s-eye view (BEV) representation; Cameras; Feature extraction; Laser radar; Multi-camera; Task analysis; Three-dimensional displays; Transformers
• ISSN: 0162-8828; 1939-3539; 1939-3539; 2160-9292
• DOI: 10.1109/TPAMI.2024.3414835

Recently, perception task based on Bird's-Eye View (BEV) representation has drawn more and more attention, and BEV representation is promising as the foundation for next-generation Autonomous Vehicle (AV) perception. However, most existing BEV solutions either require considerable resources to execute on-vehicle inference or suffer from modest performance. This paper proposes a simple yet effective framework, termed Fast-BEV, which is capable of performing faster BEV perception on the on-vehicle chips. Towards this goal, we first empirically find that the BEV representation can be sufficiently powerful without expensive transformer based transformation or depth representation. Our Fast-BEV consists of five parts, we innovatively propose (1) a lightweight deployment-friendly view transformation which fast transfers 2D image features to 3D voxel space, (2) a multi-scale image encoder which leverages multi-scale information for better performance, (3) an efficient BEV encoder which is particularly designed to speed up on-vehicle inference. We further introduce (4) a strong data augmentation strategy for both image and BEV space to avoid over-fitting, (5) a multi-frame feature fusion mechanism to leverage the temporal information. Among them, (1) and (3) enable Fast-BEV to be fast inference and deployment friendly on the on-vehicle chips, (2), (4) and (5) ensure that Fast-BEV has competitive performance. All these make Fast-BEV a solution with high performance, fast inference speed, and deployment-friendly on the on-vehicle chips of autonomous driving. Through experiments, on 2080Ti platform, our R50 model can run 52.6 FPS with 47.3% NDS on the nuScenes validation set, exceeding the 41.3 FPS and 47.5% NDS of the BEVDepth-R50 model (Li et al. 2022) and 30.2 FPS and 45.7% NDS of the BEVDet4D-R50 model (J. Huang and G. Huang, 2022). Our largest model (R101@900×1600) establishes a competitive 53.5% NDS on the nuScenes validation set. We further develop a benchmark with considerable accuracy and efficiency on current popular on-vehicle chips.