Object-Centric LiDAR-to-Radar Distillation for 3D Object Detection

Abstract

Radar is an essential sensor for autonomous driving due to its cost-effectiveness and robustness under adverse weather conditions. However, 3D radar points are inherently sparse, noisy, and lack elevation information. As a result, they provide much weaker geometric cues than LiDAR measurements, making reliable representation learning fundamentally challenging. To address this issue, prior works have explored LiDAR-to-radar distillation, enriching radar representations with dense LiDAR geometric priors. However, the substantial modality gap between LiDAR and radar may make such fine-grained alignment unnecessarily restrictive and limit the effectiveness of knowledge transfer. In this letter, we present an object-centric distillation framework that emphasizes semantically meaningful object-level cues rather than dense voxel-level matching. Specifically, we perform object-wise distillation by aggregating teacher and student features within each object region using Gaussian-weighted pooling. We further introduce density-aware curriculum weighting, which adaptively modulates the distillation strength according to the radar point density of each object. Experimental results on the nuScenes dataset show that the proposed method surpasses RadarDistill by 32.1% in relative mAP, which validates the effectiveness of the proposed framework.