Color constancy using 3D scene geometry

The aim of color constancy is to remove the effect of the color of the light source. As color constancy is inherently an ill-posed problem, most of the existing color constancy algorithms are based on specific imaging assumptions such as the grey-world and white patch assumptions. In this paper, 3D geometry models are used to determine which color constancy method to use for the different geometrical regions found in images. To this end, images are first classified into stages (rough 3D geometry models). According to the stage models, images are divided into different regions using hard and soft segmentation. After that, the best color constancy algorithm is selected for each geometry segment. As a result, light source estimation is tuned to the global scene geometry. Our algorithm opens the possibility to estimate the remote scene illumination color, by distinguishing nearby light source from distant illuminants. Experiments on large scale image datasets show that the proposed algorithm outperforms state-of-the-art single color constancy algorithms with an improvement of almost 14% of median angular error. When using an ideal classifier (i.e, all of the test images are correctly classified into stages), the performance of the proposed method achieves an improvement of 31% of median angular error compared to the best-performing single color constancy algorithm.