# calibfusion: Transformer-Based Differentiable Calibration for Radar-Camera Fusion Detection in Water-Surface Environments

arXiv: https://arxiv.org/abs/2603.06670 · HTML: https://mlsanity.com/paper/2603.06670/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 25 references that are in the map.
- First posted on arXiv 3 Mar 2026.

## Builds on (most cited first, 18 of 18)

- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [DETRs Beat YOLOs on Real-time Object Detection](https://mlsanity.com/paper/2304.08069/) (2023)
- [YOLOv10: Real-Time End-to-End Object Detection](https://mlsanity.com/paper/2405.14458/) (2024)
- [AutoAlign: Pixel-Instance Feature Aggregation for Multi-Modal 3D Object Detection](https://mlsanity.com/paper/2201.06493/) (2022)
- [CRAFT: Camera-Radar 3D Object Detection with Spatio-Contextual Fusion Transformer](https://mlsanity.com/paper/2209.06535/) (2022)
- [CARRADA Dataset: Camera and Automotive Radar with Range-Angle-Doppler Annotations](https://mlsanity.com/paper/2005.01456/) (2020)
- [Radar-Camera Fusion for Object Detection and Semantic Segmentation in Autonomous Driving: A Comprehensive Review](https://mlsanity.com/paper/2304.10410/) (2023)
- [WaterScenes: A Multi-Task 4D Radar-Camera Fusion Dataset and Benchmarks for Autonomous Driving on Water Surfaces](https://mlsanity.com/paper/2307.06505/) (2023)
- [DXQ-Net: Differentiable LiDAR-Camera Extrinsic Calibration Using Quality-aware Flow](https://mlsanity.com/paper/2203.09385/) (2022)
- [Achelous: A Fast Unified Water-surface Panoptic Perception Framework based on Fusion of Monocular Camera and 4D mmWave Radar](https://mlsanity.com/paper/2307.07102/) (2023)
- [Targetless Rotational Auto-Calibration of Radar and Camera for Intelligent Transportation Systems](https://mlsanity.com/paper/1904.08743/) (2019)
- [Radar and Camera Fusion for Object Detection and Tracking: A Comprehensive Survey](https://mlsanity.com/paper/2410.19872/) (2024)
- [Uncertainty-Aware Online Extrinsic Calibration: A Conformal Prediction Approach](https://mlsanity.com/paper/2501.06878/) (2025)
- [A Continuous-Time Approach for 3D Radar-to-Camera Extrinsic Calibration](https://mlsanity.com/paper/2103.07505/) (2021)
- [RC-AutoCalib: An End-to-End Radar-Camera Automatic Calibration Network](https://mlsanity.com/paper/2505.22427/) (2025)

## Abstract (arXiv)

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.