  Although multi-view 3D object detection based on the Bird's-Eye-View (BEV)
paradigm has garnered widespread attention as an economical and
deployment-friendly perception solution for autonomous driving, there is still
a performance gap compared to LiDAR-based methods. In recent years, several
cross-modal distillation methods have been proposed to transfer beneficial
information from teacher models to student models, with the aim of enhancing
performance. However, these methods face challenges due to discrepancies in
feature distribution originating from different data modalities and network
structures, making knowledge transfer exceptionally challenging. In this paper,
we propose a Foreground Self-Distillation (FSD) scheme that effectively avoids
the issue of distribution discrepancies, maintaining remarkable distillation
effects without the need for pre-trained teacher models or cumbersome
distillation strategies. Additionally, we design two Point Cloud
Intensification (PCI) strategies to compensate for the sparsity of point clouds
by frame combination and pseudo point assignment. Finally, we develop a
Multi-Scale Foreground Enhancement (MSFE) module to extract and fuse
multi-scale foreground features by predicted elliptical Gaussian heatmap,
further improving the model's performance. We integrate all the above
innovations into a unified framework named FSD-BEV. Extensive experiments on
the nuScenes dataset exhibit that FSD-BEV achieves state-of-the-art
performance, highlighting its effectiveness. The code and models are available
at: https://github.com/CocoBoom/fsd-bev.
