Sensor Fusion-Based Autoencoder Feature Distillation for 3D Object Detection

Knowledge distillation is a widely adopted model compression method aimed at narrowing the performance gap between a high-capacity teacher network and a lightweight student network. However, in the context of sensor fusion-based 3D object detection, existing distillation methods predominantly emphasize accuracy enhancement through the introduction of multiple loss functions, which often leads to overly complex training procedures. To address this limitation, we propose a sensor fusion-based feature distillation framework tailored for camera and radar modalities. Our proposed method utilizes an autoencoder to facilitate efficient knowledge transfer from the teacher to the student model. Additionally, we introduce image-context and radar-context knowledge distillation strategies to capture and transfer modality-specific features effectively. We demonstrate the effectiveness of the proposed method on the nuScenes dataset using a ResNet-based architecture.