Color constancy methods often struggle to generalize across different camera sensors due to varying spectral sensitivities. We present GCC, which leverages diffusion models to inpaint color checkers into images for illumination estimation. Our key innovations include (1) a single-step deterministic inference approach that inpaints color checkers reflecting scene illumination, (2) a Laplacian decomposition technique that preserves checker structure while allowing illumination-dependent color adaptation, and (3) a mask-based data augmentation strategy for handling imprecise color checker annotations. By harnessing rich priors from pre-trained diffusion models, GCC demonstrates strong robustness in challenging cross-camera scenarios. These results highlight our method's effective generalization capability across different camera characteristics without requiring sensor-specific training, making it a versatile and practical solution for real-world applications.

翻译：颜色恒常性方法常因相机传感器光谱敏感度的差异而在跨设备泛化时面临挑战。本文提出GCC方法，利用扩散模型将色卡修复至图像中以实现光照估计。我们的核心创新包括：（1）采用单步确定性推理方法，修复反映场景光照的色卡；（2）提出拉普拉斯分解技术，在保持色卡结构的同时实现光照依赖的颜色自适应；（3）设计基于掩码的数据增强策略以处理不精确的色卡标注。通过利用预训练扩散模型的丰富先验知识，GCC在具有挑战性的跨相机场景中展现出强大的鲁棒性。实验结果表明，该方法无需针对特定传感器进行训练即可有效适应不同相机特性，为实际应用提供了通用且实用的解决方案。