计算机科学
人工智能
计算机视觉
网格
感知
分割
利用
数学
计算机安全
神经科学
生物
几何学
作者
Zhiqi Li,Wenhai Wang,Hongyang Li,Enze Xie,Chonghao Sima,Tong Lu,Yu Qiao,Jifeng Dai
标识
DOI:10.1007/978-3-031-20077-9_1
摘要
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 test set, which is 9.0 points higher than previous best arts and on par with the performance of LiDAR-based baselines. The code is available at https://github.com/zhiqi-li/BEVFormer .
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