Abstract

Estimating reflectance and natural illumination from a single image of an object of known shape is a challenging task due to the ambiguities between reflectance and illumination. Although th ere is an inherent limitation in what can be recovered as the reflectance band-limits the illumination, explicitly estimat- ing both is desirable for many computer vision applications. Achieving this esti- mation requires that we derive and impose strong constraints on both variables. We introduce a probabilistic formulation that seamlessly incorporates such con- straints as priors to arrive at the maximum a posteriori estimates of reflectance and natural illumination. We begin by showing that reflectance m odulates the natural illumination in a way that increases its entropy. Based on this observation, we impose a prior on the illumination that favors lower entropy while conforming to natural image statistics. We also impose a prior on the reflectance based on the directional statistics BRDF model that constrains the estimate to lie within the bounds and variability of real-world materials. Experimental results on a number of synthetic and real images show that the method is able to achieve accurate joint estimation for different combinations of materials and lighting.