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 geo-
metrical regions found in images. To this end, images are
first classified into stages (rough 3D geometry models). Ac-
cording to the stage models, images are divided into differ-
ent regions using hard and soft segmentation. After that, the
best color constancy algorithm is selected for each geome-
try segment. As a result, light source estimation is tuned to
the global scene geometry. Our algorithm opens the pos-
sibility to estimate the remote scene illumination color, by
distinguishing nearby light source from distant illuminants.
Experiments on large scale image datasets show...