  The inherent noisy and sparse characteristics of radar data pose challenges
in finding effective representations for 3D object detection. In this paper, we
propose RadarDistill, a novel knowledge distillation (KD) method, which can
improve the representation of radar data by leveraging LiDAR data. RadarDistill
successfully transfers desirable characteristics of LiDAR features into radar
features using three key components: Cross-Modality Alignment (CMA),
Activation-based Feature Distillation (AFD), and Proposal-based Feature
Distillation (PFD). CMA enhances the density of radar features by employing
multiple layers of dilation operations, effectively addressing the challenge of
inefficient knowledge transfer from LiDAR to radar. AFD selectively transfers
knowledge based on regions of the LiDAR features, with a specific focus on
areas where activation intensity exceeds a predefined threshold. PFD similarly
guides the radar network to selectively mimic features from the LiDAR network
within the object proposals. Our comparative analyses conducted on the nuScenes
datasets demonstrate that RadarDistill achieves state-of-the-art (SOTA)
performance for radar-only object detection task, recording 20.5% in mAP and
43.7% in NDS. Also, RadarDistill significantly improves the performance of the
camera-radar fusion model.
