BEVFormer v2: Adapting Modern Image Backbones to Bird’s-Eye-View Recognition via Perspective Supervision

Chenyu Yang^1*     Yuntao Chen^2*     Hao Tian^3*     Chenxin Tao¹     Xizhou Zhu³     Zhaoxiang Zhang^2,4     Gao Huang¹

 Hongyang Li⁵     Yu Qiao⁵     Lewei Lu³    Jie Zhou¹    Jifeng Dai^{1,5 ?}

¹Tsinghua University  ²Centre for Artificial Intelligence and Robotics, HKISI CAS  ³SenseTime Research  ⁴Institute of Automation, Chinese Academy of Science (CASIA) ⁵Shanghai Artificial Intelligence Laboratory

 {yangcy19, tcx20}@mails.tsinghua.edu.cn, chenyuntao08@gmail.com, tianhao2@senseauto.com 

{zhuwalter, luotto}@sensetime.com, zhaoxiang.zhang@ia.ac.cn

 {gaohuang, jzhou, daijifeng}@tsinghua.edu.cn, {lihongyang, qiaoyu}@pjlab.org.cn

Abstract 

We present a novel bird’s-eye-view (BEV) detector with perspective supervision, which converges faster and better suits modern image backbones. Existing state-of-theart BEV detectors are often tied to certain depth pretrained backbones like VoVNet, hindering the synergy between booming image backbones and BEV detectors. To address this limitation, we prioritize easing the optimization of BEV detectors by introducing perspective view supervision. To this end, we propose a two-stage BEV detector, where proposals from the perspective head are fed into the bird’s-eye-view head for final predictions. To evaluate the effectiveness of our model, we conduct extensive ablation studies focusing on the form of supervision and the generality of the proposed detector. The proposed method is verified with a wide spectrum of traditional and modern image backbones and achieves new SoTA results on the large-scale nuScenes dataset. The code shall be released soon.