Shape and Spatially-Varying Reflectance Estimation From Virtual Exemplars

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.



Zhuo Hui, Aswin C. Sankaranarayanan

ICCP 2015


Zhuo Hui, Aswin C. Sankaranarayanan

IEEE Trans. Pattern Analysis and Machine Intelligence (PAMI), volume 39, no. 10, pp. 2060-2073, 2017


Supplementary Materials

We provide additional results in the supplementary docs.

Source Code

We provide our source code at here.


Presentation slides