  In the field of 3D object detection for autonomous driving, LiDAR-Camera (LC)
fusion is the top-performing sensor configuration. Still, LiDAR is relatively
high cost, which hinders adoption of this technology for consumer automobiles.
Alternatively, camera and radar are commonly deployed on vehicles already on
the road today, but performance of Camera-Radar (CR) fusion falls behind LC
fusion. In this work, we propose Camera-Radar Knowledge Distillation (CRKD) to
bridge the performance gap between LC and CR detectors with a novel
cross-modality KD framework. We use the Bird's-Eye-View (BEV) representation as
the shared feature space to enable effective knowledge distillation. To
accommodate the unique cross-modality KD path, we propose four distillation
losses to help the student learn crucial features from the teacher model. We
present extensive evaluations on the nuScenes dataset to demonstrate the
effectiveness of the proposed CRKD framework. The project page for CRKD is
https://song-jingyu.github.io/CRKD.
