A Dictionary-based Approach for Estimating Shape and Spatially-Varying Reflectance

TL;DR

This paper introduces a non-iterative, dictionary-based method for estimating an object's shape and spatially-varying reflectance from multiple images under fixed viewpoint and changing illumination, outperforming existing techniques.

Contribution

It proposes a novel, non-iterative framework that estimates surface normals and BRDFs using a known dictionary, avoiding common optimization challenges.

Findings

Outperforms existing methods on simulated scenes

Effective in real-world scenarios

Requires no iterative optimization or careful initialization

Abstract

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.