# Near-Light Color Photometric Stereo for Mono-Chromatic Non-Lambertian Surfaces

arXiv: https://arxiv.org/abs/2601.12666 · HTML: https://mlsanity.com/paper/2601.12666/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 23 references that are in the map.
- First posted on arXiv 19 Jan 2026.

## Builds on (most cited first, 11 of 11)

- [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://mlsanity.com/paper/2201.05989/) (2022)
- [Implicit Neural Representations with Periodic Activation Functions](https://mlsanity.com/paper/2006.09661/) (2020)
- [CNN-PS: CNN-based Photometric Stereo for General Non-Convex Surfaces](https://mlsanity.com/paper/1808.10093/) (2018)
- [Scalable, Detailed and Mask-Free Universal Photometric Stereo](https://mlsanity.com/paper/2303.15724/) (2023)
- [Deep Photometric Stereo for Non-Lambertian Surfaces](https://mlsanity.com/paper/2007.13145/) (2020)
- [Normal Integration: A Survey](https://mlsanity.com/paper/1709.05940/) (2017)
- [LED-based Photometric Stereo: Modeling, Calibration and Numerical Solution](https://mlsanity.com/paper/1707.01018/) (2017)
- [Fast Light-Weight Near-Field Photometric Stereo](https://mlsanity.com/paper/2203.16515/) (2022)
- [Single-image RGB Photometric Stereo With Spatially-varying Albedo](https://mlsanity.com/paper/1609.04079/) (2016)
- [Physics-Free Spectrally Multiplexed Photometric Stereo under Unknown Spectral Composition](https://mlsanity.com/paper/2410.20716/) (2024)
- [3D Face Reconstruction Using Color Photometric Stereo with Uncalibrated Near Point Lights](https://mlsanity.com/paper/1904.02605/) (2019)

## Abstract (arXiv)

Color photometric stereo enables single-shot surface reconstruction, extending conventional photometric stereo that requires multiple images of a static scene under varying illumination to dynamic scenarios. However, most existing approaches assume ideal distant lighting and Lambertian reflectance, leaving more practical near-light conditions and non-Lambertian surfaces underexplored. To overcome this limitation, we propose a framework that leverages neural implicit representations for depth and BRDF modeling under the assumption of mono-chromaticity (uniform chromaticity and homogeneous material), which alleviates the inherent ill-posedness of color photometric stereo and allows for detailed surface recovery from just one image. Furthermore, we design a compact optical tactile sensor to validate our approach. Experiments on both synthetic and real-world datasets demonstrate that our method achieves accurate and robust surface reconstruction.