# CRKD: Enhanced Camera-Radar Object Detection with Cross-modality Knowledge Distillation Source: [CRKD: Enhanced Camera-Radar Object Detection with Cross-modality Knowledge Distillation]() Collection index: [llms.txt]() Data updated: 2026-01-04T04:42:18.162Z Authors: Lingjun Zhao, Jingyu Song, Katherine A. Skinner Published: 2024-03-28T02:39:45.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [3D Object Detection on nuScenes (val)](): 3D Object Detection; nuScenes (val); NDS: 57.3. - [3D Object Detection on nuScenes (test)](): 3D Object Detection; nuScenes (test); mAP: 48.7. ## Original sources and implementation [Original paper]() [PDF]() [Implementation]()