  Contemporary approaches frame the color constancy problem as learning camera
specific illuminant mappings. While high accuracy can be achieved on camera
specific data, these models depend on camera spectral sensitivity and typically
exhibit poor generalisation to new devices. Additionally, regression methods
produce point estimates that do not explicitly account for potential
ambiguities among plausible illuminant solutions, due to the ill-posed nature
of the problem. We propose a Bayesian framework that naturally handles color
constancy ambiguity via a multi-hypothesis strategy. Firstly, we select a set
of candidate scene illuminants in a data-driven fashion and apply them to a
target image to generate of set of corrected images. Secondly, we estimate, for
each corrected image, the likelihood of the light source being achromatic using
a camera-agnostic CNN. Finally, our method explicitly learns a final
illumination estimate from the generated posterior probability distribution.
Our likelihood estimator learns to answer a camera-agnostic question and thus
enables effective multi-camera training by disentangling illuminant estimation
from the supervised learning task. We extensively evaluate our proposed
approach and additionally set a benchmark for novel sensor generalisation
without re-training. Our method provides state-of-the-art accuracy on multiple
public datasets (up to 11% median angular error improvement) while maintaining
real-time execution.
