  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-the-art BEV detectors are often tied to certain depth
pre-trained 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 space 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.
