IMKD: Intensity-Aware Multi-Level Knowledge Distillation for Camera-Radar Fusion

TL;DR

IMKD introduces a multi-level, intensity-aware knowledge distillation framework for radar-camera fusion that preserves sensor-specific features and enhances 3D object detection performance without LiDAR.

Contribution

It proposes a novel three-stage distillation strategy that maintains sensor characteristics and improves fusion effectiveness in radar-camera 3D detection.

Findings

Achieves 67.0% NDS and 61.0% mAP on nuScenes benchmark.

Outperforms all prior distillation-based radar-camera fusion methods.

Demonstrates effective feature alignment and calibration across modalities.

Abstract

High-performance Radar-Camera 3D object detection can be achieved by leveraging knowledge distillation without using LiDAR at inference time. However, existing distillation methods typically transfer modality-specific features directly to each sensor, which can distort their unique characteristics and degrade their individual strengths. To address this, we introduce IMKD, a radar-camera fusion framework based on multi-level knowledge distillation that preserves each sensor's intrinsic characteristics while amplifying their complementary strengths. IMKD applies a three-stage, intensity-aware distillation strategy to enrich the fused representation across the architecture: (1) LiDAR-to-Radar intensity-aware feature distillation to enhance radar representations with fine-grained structural cues, (2) LiDAR-to-Fused feature intensity-guided distillation to selectively highlight useful geometry and depth information at the fusion level, fostering complementarity between the modalities rather than forcing them to align, and (3) Camera-Radar intensity-guided fusion mechanism that facilitates effective feature alignment and calibration. Extensive experiments on the nuScenes benchmark show that IMKD reaches 67.0% NDS and 61.0% mAP, outperforming all prior distillation-based radar-camera fusion methods. Our code and models are available at https://github.com/dfki-av/IMKD/.