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 https://github.com/zhiqi-li/BEVFormer.

本文提出了新的 BEVFormer 框架，该框架学习使用时空变形器的统一 BEV 表示，以支持多个自主驾驶感知任务。在 nuScenes 测试集中，该方法达到了 56.9％的最新技术水平，且在低能见度条件下显着提高了速度估计和对象召回的准确性。