  Detecting 3D objects from multi-view images is a fundamental problem in 3D
computer vision. Recently, significant breakthrough has been made in multi-view
3D detection tasks. However, the unprecedented detection performance of these
vision BEV (bird's-eye-view) detection models is accompanied with enormous
parameters and computation, which make them unaffordable on edge devices. To
address this problem, in this paper, we propose a structured knowledge
distillation framework, aiming to improve the efficiency of modern vision-only
BEV detection models. The proposed framework mainly includes: (a)
spatial-temporal distillation which distills teacher knowledge of information
fusion from different timestamps and views, (b) BEV response distillation which
distills teacher response to different pillars, and (c) weight-inheriting which
solves the problem of inconsistent inputs between students and teacher in
modern transformer architectures. Experimental results show that our method
leads to an average improvement of 2.16 mAP and 2.27 NDS on the nuScenes
benchmark, outperforming multiple baselines by a large margin.
