  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 nor depth representation.
Our Fast-BEV consists of five parts, We novelly propose (1) a lightweight
deployment-friendly view transformation which fast transfers 2D image feature
to 3D voxel space, (2) an 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. 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 and 30.2 FPS and 45.7% NDS of
the BEVDet4D-R50 model. Our largest model (R101@900x1600) 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. The code is released at:
https://github.com/Sense-GVT/Fast-BEV.
