  Recently, Bird's-Eye-View (BEV) representation has gained increasing
attention in multi-view 3D object detection, which has demonstrated promising
applications in autonomous driving. Although multi-view camera systems can be
deployed at low cost, the lack of depth information makes current approaches
adopt large models for good performance. Therefore, it is essential to improve
the efficiency of BEV 3D object detection. Knowledge Distillation (KD) is one
of the most practical techniques to train efficient yet accurate models.
However, BEV KD is still under-explored to the best of our knowledge. Different
from image classification tasks, BEV 3D object detection approaches are more
complicated and consist of several components. In this paper, we propose a
unified framework named BEV-LGKD to transfer the knowledge in the
teacher-student manner. However, directly applying the teacher-student paradigm
to BEV features fails to achieve satisfying results due to heavy background
information in RGB cameras. To solve this problem, we propose to leverage the
localization advantage of LiDAR points. Specifically, we transform the LiDAR
points to BEV space and generate the foreground mask and view-dependent mask
for the teacher-student paradigm. It is to be noted that our method only uses
LiDAR points to guide the KD between RGB models. As the quality of depth
estimation is crucial for BEV perception, we further introduce depth
distillation to our framework. Our unified framework is simple yet effective
and achieves a significant performance boost. Code will be released.

Computer Vision and Pattern Recognition