  In this paper, we present BEVerse, a unified framework for 3D perception and
prediction based on multi-camera systems. Unlike existing studies focusing on
the improvement of single-task approaches, BEVerse features in producing
spatio-temporal Birds-Eye-View (BEV) representations from multi-camera videos
and jointly reasoning about multiple tasks for vision-centric autonomous
driving. Specifically, BEVerse first performs shared feature extraction and
lifting to generate 4D BEV representations from multi-timestamp and multi-view
images. After the ego-motion alignment, the spatio-temporal encoder is utilized
for further feature extraction in BEV. Finally, multiple task decoders are
attached for joint reasoning and prediction. Within the decoders, we propose
the grid sampler to generate BEV features with different ranges and
granularities for different tasks. Also, we design the method of iterative flow
for memory-efficient future prediction. We show that the temporal information
improves 3D object detection and semantic map construction, while the
multi-task learning can implicitly benefit motion prediction. With extensive
experiments on the nuScenes dataset, we show that the multi-task BEVerse
outperforms existing single-task methods on 3D object detection, semantic map
construction, and motion prediction. Compared with the sequential paradigm,
BEVerse also favors in significantly improved efficiency. The code and trained
models will be released at https://github.com/zhangyp15/BEVerse.
