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                          科學研究

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

                          發表會議及期刊:arXiv

                          Chenyu Yang1*     Yuntao Chen2*     Hao Tian3*     Chenxin Tao1     Xizhou Zhu3     Zhaoxiang Zhang2,4     Gao Huang1

                           Hongyang Li5     Yu Qiao5     Lewei Lu3    Jie Zhou1    Jifeng Dai1,5 ?

                          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.

                          comm@pjlab.org.cn

                          上海市徐匯區云錦路701號西岸國際人工智能中心37-38層

                          滬ICP備2021009351號-1

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