Zero-Shot Color Constancy by Estimating Albedo

Computational color constancy (CC) aims to recover the true colors of a scene despite changes in illumination. While recent cross-camera deep learning methods have made impressive progress, their training relies on relatively small RAW datasets. In this work, we introduce a zero-shot color constancy framework grounded in intrinsic image decomposition (IID). Our method estimates the albedo and infers the illumination map from a pre-trained IID model, followed by chromaticity clustering. This design allows us to avoid any fine-tuning on CC datasets. Experiments on the Gehler-Shi and NUS-8 datasets show that our approach achieves performance comparable to cross-dataset SOTA performance, while maintaining robustness to camera and dataset variations. These results highlight the potential of transferring IID-learned illumination priors to achieve truly sensor-agnostic color constancy.