  We present a technique for estimating the shape and reflectance of an object
in terms of its surface normals and spatially-varying BRDF. We assume that
multiple images of the object are obtained under fixed view-point and varying
illumination, i.e, the setting of photometric stereo. Assuming that the BRDF at
each pixel lies in the non-negative span of a known BRDF dictionary, we derive
a per-pixel surface normal and BRDF estimation framework that requires neither
iterative optimization techniques nor careful initialization, both of which are
endemic to most state-of-the-art techniques. We showcase the performance of our
technique on a wide range of simulated and real scenes where we outperform
competing methods.
