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.
@article{arxiv.1503.04265,
title = {A Dictionary-based Approach for Estimating Shape and Spatially-Varying Reflectance},
author = {Zhuo Hui and Aswin C. Sankaranarayanan},
journal= {arXiv preprint arXiv:1503.04265},
year = {2015}
}