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

arXiv: https://arxiv.org/abs/1503.04265 · HTML: https://mlsanity.com/paper/1503.04265/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Mar 2015.

## Built on by (most cited first, 2 of 2)

- [SVBRDF Recovery From a Single Image With Highlights using a Pretrained Generative Adversarial Network](https://mlsanity.com/paper/2111.00943/) (2021)
- [MatPedia: A Universal Generative Foundation for High-Fidelity Material Synthesis](https://mlsanity.com/paper/2511.16957/) (2025)

## Abstract (arXiv)

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.