CRKD: クロスモダリティ知識蒸留によるカメラ・レーダー物体検出の強化
CRKD: Enhanced Camera-Radar Object Detection with Cross-modality Knowledge Distillation
LiDAR-カメラ融合の高性能をカメラ-レーダー検出器に知識蒸留で転移し、BEV表現を共有空間として4つの蒸留損失で性能差を縮める手法を提案。
著者: Lingjun Zhao, Jingyu Song, Katherine A. Skinner
分類: cs.CV, cs.RO
原文アブストラクト
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.
