calibfusion: Transformer-Based Differentiable Calibration for Radar-Camera Fusion Detection in Water-Surface Environments
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866910044027617280 |
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| author | Wan, Yuting Sun, Liguo Hao, Jiuwu LV, Pin |
| author_facet | Wan, Yuting Sun, Liguo Hao, Jiuwu LV, Pin |
| contents | Millimeter-wave (mmWave) Radar--Camera fusion improves perception under adverse illumination and weather, but its performance is sensitive to Radar--Camera extrinsic calibration: residual misalignment biases Radar-to-image projection and degrades cross-modal aggregation for downstream 2D detection. Existing calibration and auto-calibration methods are mainly developed for road and urban scenes with abundant structures and object constraints, whereas water-surface environments feature large textureless regions, sparse and intermittent targets, and wave-/specular-induced Radar clutter, which weakens explicit object-centric matching.
We propose CalibFusion, a calibration-conditioned Radar--Camera fusion detector that learns implicit extrinsic refinement end-to-end with the detection objective. CalibFusion builds a multi-frame persistence-aware Radar density representation with intensity weighting and Doppler-guided suppression of fast-varying clutter. A cross-modal transformer interaction module predicts a confidence-gated refinement of the initial extrinsics, which is integrated through a differentiable projection-and-splatting operator to generate calibration-conditioned image-plane Radar features.
Experiments on WaterScenes and FLOW show improved fusion-based 2D detection and robustness under synthetic miscalibration, supported by sensitivity analyses and qualitative Radar-to-image overlays. Results on nuScenes indicate that the refinement mechanism transfers beyond water-surface scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_06670 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | calibfusion: Transformer-Based Differentiable Calibration for Radar-Camera Fusion Detection in Water-Surface Environments Wan, Yuting Sun, Liguo Hao, Jiuwu LV, Pin Computer Vision and Pattern Recognition Artificial Intelligence Millimeter-wave (mmWave) Radar--Camera fusion improves perception under adverse illumination and weather, but its performance is sensitive to Radar--Camera extrinsic calibration: residual misalignment biases Radar-to-image projection and degrades cross-modal aggregation for downstream 2D detection. Existing calibration and auto-calibration methods are mainly developed for road and urban scenes with abundant structures and object constraints, whereas water-surface environments feature large textureless regions, sparse and intermittent targets, and wave-/specular-induced Radar clutter, which weakens explicit object-centric matching. We propose CalibFusion, a calibration-conditioned Radar--Camera fusion detector that learns implicit extrinsic refinement end-to-end with the detection objective. CalibFusion builds a multi-frame persistence-aware Radar density representation with intensity weighting and Doppler-guided suppression of fast-varying clutter. A cross-modal transformer interaction module predicts a confidence-gated refinement of the initial extrinsics, which is integrated through a differentiable projection-and-splatting operator to generate calibration-conditioned image-plane Radar features. Experiments on WaterScenes and FLOW show improved fusion-based 2D detection and robustness under synthetic miscalibration, supported by sensitivity analyses and qualitative Radar-to-image overlays. Results on nuScenes indicate that the refinement mechanism transfers beyond water-surface scenarios. |
| title | calibfusion: Transformer-Based Differentiable Calibration for Radar-Camera Fusion Detection in Water-Surface Environments |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2603.06670 |