# LUCES: A Dataset for Near-Field Point Light Source Photometric Stereo

arXiv: https://arxiv.org/abs/2104.13135 · HTML: https://mlsanity.com/paper/2104.13135/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 44 references that are in the map.
- First posted on arXiv 27 Apr 2021.

## Builds on (most cited first, 8 of 8)

- [PS-FCN: A Flexible Learning Framework for Photometric Stereo](https://mlsanity.com/paper/1807.08696/) (2018)
- [CNN-PS: CNN-based Photometric Stereo for General Non-Convex Surfaces](https://mlsanity.com/paper/1808.10093/) (2018)
- [A Differential Volumetric Approach to Multi-View Photometric Stereo](https://mlsanity.com/paper/1811.01984/) (2018)
- [Normal Integration: A Survey](https://mlsanity.com/paper/1709.05940/) (2017)
- [From Shading to Local Shape](https://mlsanity.com/paper/1310.2916/) (2013)
- [LED-based Photometric Stereo: Modeling, Calibration and Numerical Solution](https://mlsanity.com/paper/1707.01018/) (2017)
- [A CNN Based Approach for the Near-Field Photometric Stereo Problem](https://mlsanity.com/paper/2009.05792/) (2020)
- [Least squares surface reconstruction on arbitrary domains](https://mlsanity.com/paper/2007.08661/) (2020)

## Built on by (most cited first, 15 of 15)

- [S3-NeRF: Neural Reflectance Field from Shading and Shadow under a Single Viewpoint](https://mlsanity.com/paper/2210.08936/) (2022)
- [Fast Light-Weight Near-Field Photometric Stereo](https://mlsanity.com/paper/2203.16515/) (2022)
- [A CNN Based Approach for the Point-Light Photometric Stereo Problem](https://mlsanity.com/paper/2210.04655/) (2022)
- [Deep Learning Methods for Calibrated Photometric Stereo and Beyond](https://mlsanity.com/paper/2212.08414/) (2022)
- [Light of Normals: Unified Feature Representation for Universal Photometric Stereo](https://mlsanity.com/paper/2506.18882/) (2025)
- [Edge-preserving Near-light Photometric Stereo with Neural Surfaces](https://mlsanity.com/paper/2207.04622/) (2022)
- [Differentiable Display Photometric Stereo](https://mlsanity.com/paper/2306.13325/) (2023)
- [MERLiN: Single-Shot Material Estimation and Relighting for Photometric Stereo](https://mlsanity.com/paper/2409.00674/) (2024)
- [A Real-world Display Inverse Rendering Dataset](https://mlsanity.com/paper/2508.14411/) (2025)
- [A Neural Height-Map Approach for the Binocular Photometric Stereo Problem](https://mlsanity.com/paper/2311.05958/) (2023)
- [LUCES-MV: A Multi-View Dataset for Near-Field Point Light Source Photometric Stereo](https://mlsanity.com/paper/2412.16737/) (2024)
- [Monocular Normal Estimation via Shading Sequence Estimation](https://mlsanity.com/paper/2602.09929/) (2026)
- [Feature-Preserving Mesh Decimation for Normal Integration](https://mlsanity.com/paper/2504.00867/) (2025)
- [Geometry Meets Light: Leveraging Geometric Priors for Universal Photometric Stereo under Limited Multi-Illumination Cues](https://mlsanity.com/paper/2511.13015/) (2025)
- [Near-light Photometric Stereo with Symmetric Lights](https://mlsanity.com/paper/2603.16404/) (2026)

## Abstract (arXiv)

Three-dimensional reconstruction of objects from shading information is a challenging task in computer vision. As most of the approaches facing the Photometric Stereo problem use simplified far-field assumptions, real-world scenarios have essentially more complex physical effects that need to be handled for accurately reconstructing the 3D shape. An increasing number of methods have been proposed to address the problem when point light sources are assumed to be nearby the target object. The proximity of the light sources complicates the modeling of the image formation as the light behaviour requires non-linear parameterisation to describe its propagation and attenuation. To understand the capability of the approaches dealing with this near-field scenario, the literature till now has used synthetically rendered photometric images or minimal and very customised real-world data. In order to fill the gap in evaluating near-field photometric stereo methods, we introduce LUCES the first real-world 'dataset for near-fieLd point light soUrCe photomEtric Stereo' of 14 objects of a varying of materials. A device counting 52 LEDs has been designed to lit each object positioned 10 to 30 centimeters away from the camera. Together with the raw images, in order to evaluate the 3D reconstructions, the dataset includes both normal and depth maps for comparing different features of the retrieved 3D geometry. Furthermore, we evaluate the performance of the latest near-field Photometric Stereo algorithms on the proposed dataset to assess the SOTA method with respect to actual close range effects and object materials.