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.

通过训练基于多视角鸟瞰图（BEV）的学生检测器来模仿经过训练的基于 LiDAR 的教师检测器的特征，从而提高多视角 BEV 的表示学习，并通过有效的平衡策略和多尺度层的时间融合实现知识传输，实验证明该方法在多个多视角 BEV 模型上显著改善了学生模型，达到了流行基准 nuScenes 的最新性能。