  3D perception based on the representations learned from multi-camera
bird's-eye-view (BEV) is trending as cameras are cost-effective for mass
production in autonomous driving industry. However, there exists a distinct
performance gap between multi-camera BEV and LiDAR based 3D object detection.
One key reason is that LiDAR captures accurate depth and other geometry
measurements, while it is notoriously challenging to infer such 3D information
from merely image input. In this work, we propose to boost the representation
learning of a multi-camera BEV based student detector by training it to imitate
the features of a well-trained LiDAR based teacher detector. We propose
effective balancing strategy to enforce the student to focus on learning the
crucial features from the teacher, and generalize knowledge transfer to
multi-scale layers with temporal fusion. We conduct extensive evaluations on
multiple representative models of multi-camera BEV. Experiments reveal that our
approach renders significant improvement over the student models, leading to
the state-of-the-art performance on the popular benchmark nuScenes.
