IMKD: Intensity-Aware Multi-Level Knowledge Distillation for Camera-Radar Fusion
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866909967718547456 |
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| author | Mishra, Shashank Patil, Karan Stricker, Didier Rambach, Jason |
| author_facet | Mishra, Shashank Patil, Karan Stricker, Didier Rambach, Jason |
| contents | 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/. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2512_15581 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | IMKD: Intensity-Aware Multi-Level Knowledge Distillation for Camera-Radar Fusion Mishra, Shashank Patil, Karan Stricker, Didier Rambach, Jason Computer Vision and Pattern Recognition Machine Learning 68T45 I.2.10; I.4.8; I.5.1 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/. |
| title | IMKD: Intensity-Aware Multi-Level Knowledge Distillation for Camera-Radar Fusion |
| topic | Computer Vision and Pattern Recognition Machine Learning 68T45 I.2.10; I.4.8; I.5.1 |
| url | https://arxiv.org/abs/2512.15581 |