# A CNN Based Approach for the Point-Light Photometric Stereo Problem

arXiv: https://arxiv.org/abs/2210.04655 · HTML: https://mlsanity.com/paper/2210.04655/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 74 references that are in the map.
- First posted on arXiv 10 Oct 2022.

## Builds on (most cited first, 15 of 15)

- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [PS-FCN: A Flexible Learning Framework for Photometric Stereo](https://mlsanity.com/paper/1807.08696/) (2018)
- [Self-calibrating Deep Photometric Stereo Networks](https://mlsanity.com/paper/1903.07366/) (2019)
- [CNN-PS: CNN-based Photometric Stereo for General Non-Convex Surfaces](https://mlsanity.com/paper/1808.10093/) (2018)
- [Neural Inverse Rendering for General Reflectance Photometric Stereo](https://mlsanity.com/paper/1802.10328/) (2018)
- [A Differential Volumetric Approach to Multi-View Photometric Stereo](https://mlsanity.com/paper/1811.01984/) (2018)
- [SPLINE-Net: Sparse Photometric Stereo through Lighting Interpolation and Normal Estimation Networks](https://mlsanity.com/paper/1905.04088/) (2019)
- [Deep Photometric Stereo for Non-Lambertian Surfaces](https://mlsanity.com/paper/2007.13145/) (2020)
- [PX-NET: Simple and Efficient Pixel-Wise Training of Photometric Stereo Networks](https://mlsanity.com/paper/2008.04933/) (2020)
- [Shape and Spatially-Varying Reflectance Estimation From Virtual Exemplars](https://mlsanity.com/paper/1512.05278/) (2015)
- [LUCES: A Dataset for Near-Field Point Light Source Photometric Stereo](https://mlsanity.com/paper/2104.13135/) (2021)
- [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, 10 of 10)

- [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)
- [Sparse Views, Near Light: A Practical Paradigm for Uncalibrated Point-light Photometric Stereo](https://mlsanity.com/paper/2404.00098/) (2024)
- [NPLMV-PS: Neural Point-Light Multi-View Photometric Stereo](https://mlsanity.com/paper/2405.12057/) (2024)
- [MS-PS: A Multi-Scale Network for Photometric Stereo With a New Comprehensive Training Dataset](https://mlsanity.com/paper/2211.14118/) (2022)
- [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)
- [Event Fusion Photometric Stereo Network](https://mlsanity.com/paper/2303.00308/) (2023)
- [Depth Estimation Algorithm Based on Transformer-Encoder and Feature Fusion](https://mlsanity.com/paper/2403.01370/) (2024)
- [Near-light Photometric Stereo with Symmetric Lights](https://mlsanity.com/paper/2603.16404/) (2026)

## Abstract (arXiv)

Reconstructing the 3D shape of an object using several images under different light sources is a very challenging task, especially when realistic assumptions such as light propagation and attenuation, perspective viewing geometry and specular light reflection are considered. Many of works tackling Photometric Stereo (PS) problems often relax most of the aforementioned assumptions. Especially they ignore specular reflection and global illumination effects. In this work, we propose a CNN-based approach capable of handling these realistic assumptions by leveraging recent improvements of deep neural networks for far-field Photometric Stereo and adapt them to the point light setup. We achieve this by employing an iterative procedure of point-light PS for shape estimation which has two main steps. Firstly we train a per-pixel CNN to predict surface normals from reflectance samples. Secondly, we compute the depth by integrating the normal field in order to iteratively estimate light directions and attenuation which is used to compensate the input images to compute reflectance samples for the next iteration. Our approach sigificantly outperforms the state-of-the-art on the DiLiGenT real world dataset. Furthermore, in order to measure the performance of our approach for near-field point-light source PS data, we introduce LUCES the first real-world 'dataset for near-fieLd point light soUrCe photomEtric Stereo' of 14 objects of different materials were the effects of point light sources and perspective viewing are a lot more significant. Our approach also outperforms the competition on this dataset as well. Data and test code are available at the project page.