# Single-image RGB Photometric Stereo With Spatially-varying Albedo

arXiv: https://arxiv.org/abs/1609.04079 · HTML: https://mlsanity.com/paper/1609.04079/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2016.

## Built on by (most cited first, 5 of 5)

- [DarkGS: Learning Neural Illumination and 3D Gaussians Relighting for Robotic Exploration in the Dark](https://mlsanity.com/paper/2403.10814/) (2024)
- [Physics-Free Spectrally Multiplexed Photometric Stereo under Unknown Spectral Composition](https://mlsanity.com/paper/2410.20716/) (2024)
- [NeuralMPS: Non-Lambertian Multispectral Photometric Stereo via Spectral Reflectance Decomposition](https://mlsanity.com/paper/2211.15311/) (2022)
- [Face Inverse Rendering via Hierarchical Decoupling](https://mlsanity.com/paper/2301.06733/) (2023)
- [Near-Light Color Photometric Stereo for Mono-Chromatic Non-Lambertian Surfaces](https://mlsanity.com/paper/2601.12666/) (2026)

## Abstract (arXiv)

We present a single-shot system to recover surface geometry of objects with spatially-varying albedos, from images captured under a calibrated RGB photometric stereo setup---with three light directions multiplexed across different color channels in the observed RGB image. Since the problem is ill-posed point-wise, we assume that the albedo map can be modeled as piece-wise constant with a restricted number of distinct albedo values. We show that under ideal conditions, the shape of a non-degenerate local constant albedo surface patch can theoretically be recovered exactly. Moreover, we present a practical and efficient algorithm that uses this model to robustly recover shape from real images. Our method first reasons about shape locally in a dense set of patches in the observed image, producing shape distributions for every patch. These local distributions are then combined to produce a single consistent surface normal map. We demonstrate the efficacy of the approach through experiments on both synthetic renderings as well as real captured images.