PowerBEV: A Powerful Yet Lightweight Framework for Instance Prediction
  in Bird's-Eye View
Peizheng Li Shuxiao Ding Xieyuanli Chen Niklas Hanselmann Marius Cordts Juergen Gall
Accurately perceiving instances and predicting their future motion are key
tasks for autonomous vehicles, enabling them to navigate safely in complex
urban traffic. While bird's-eye view (BEV) representations are commonplace in
perception for autonomous driving, their potential in a motion prediction
setting is less explored. Existing approaches for BEV instance prediction from
surround cameras rely on a multi-task auto-regressive setup coupled with
complex post-processing to predict future instances in a spatio-temporally
consistent manner. In this paper, we depart from this paradigm and propose an
efficient novel end-to-end framework named POWERBEV, which differs in several
design choices aimed at reducing the inherent redundancy in previous methods.
First, rather than predicting the future in an auto-regressive fashion,
POWERBEV uses a parallel, multi-scale module built from lightweight 2D
convolutional networks. Second, we show that segmentation and centripetal
backward flow are sufficient for prediction, simplifying previous multi-task
objectives by eliminating redundant output modalities. Building on this output
representation, we propose a simple, flow warping-based post-processing
approach which produces more stable instance associations across time. Through
this lightweight yet powerful design, POWERBEV outperforms state-of-the-art
baselines on the NuScenes Dataset and poses an alternative paradigm for BEV
instance prediction. We made our code publicly available at:
https://github.com/EdwardLeeLPZ/PowerBEV.