# PS-FCN: A Flexible Learning Framework for Photometric Stereo

arXiv: https://arxiv.org/abs/1807.08696 · HTML: https://mlsanity.com/paper/1807.08696/

## Key facts

- Cited by 51 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jul 2018.

## Built on by (most cited first, 30 of 51)

- [PS-NeRF: Neural Inverse Rendering for Multi-view Photometric Stereo](https://mlsanity.com/paper/2207.11406/) (2022)
- [Shape and Material Capture at Home](https://mlsanity.com/paper/2104.06397/) (2021)
- [S3-NeRF: Neural Reflectance Field from Shading and Shadow under a Single Viewpoint](https://mlsanity.com/paper/2210.08936/) (2022)
- [Scalable, Detailed and Mask-Free Universal Photometric Stereo](https://mlsanity.com/paper/2303.15724/) (2023)
- [Neural Reflectance for Shape Recovery with Shadow Handling](https://mlsanity.com/paper/2203.12909/) (2022)
- [Neural Radiance Fields Approach to Deep Multi-View Photometric Stereo](https://mlsanity.com/paper/2110.05594/) (2021)
- [Mending Neural Implicit Modeling for 3D Vehicle Reconstruction in the Wild](https://mlsanity.com/paper/2101.06860/) (2021)
- [Universal Photometric Stereo Network using Global Lighting Contexts](https://mlsanity.com/paper/2206.02452/) (2022)
- [MVPSNet: Fast Generalizable Multi-view Photometric Stereo](https://mlsanity.com/paper/2305.11167/) (2023)
- [Self-calibrating Photometric Stereo by Neural Inverse Rendering](https://mlsanity.com/paper/2207.07815/) (2022)
- [Transparent Shape from a Single View Polarization Image](https://mlsanity.com/paper/2204.06331/) (2022)
- [LUCES: A Dataset for Near-Field Point Light Source Photometric Stereo](https://mlsanity.com/paper/2104.13135/) (2021)
- [PS-Transformer: Learning Sparse Photometric Stereo Network using Self-Attention Mechanism](https://mlsanity.com/paper/2211.11386/) (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)
- [Diffusion Knows Transparency: Repurposing Video Diffusion for Transparent Object Depth and Normal Estimation](https://mlsanity.com/paper/2512.23705/) (2025)
- [Light of Normals: Unified Feature Representation for Universal Photometric Stereo](https://mlsanity.com/paper/2506.18882/) (2025)
- [Deep DIC: Deep Learning-Based Digital Image Correlation for End-to-End Displacement and Strain Measurement](https://mlsanity.com/paper/2110.13720/) (2021)
- [Neural Architecture Search for Efficient Uncalibrated Deep Photometric Stereo](https://mlsanity.com/paper/2110.05621/) (2021)
- [DeepShadow: Neural Shape from Shadow](https://mlsanity.com/paper/2203.15065/) (2022)
- [DeepPS2: Revisiting Photometric Stereo Using Two Differently Illuminated Images](https://mlsanity.com/paper/2207.02025/) (2022)
- [DANI-Net: Uncalibrated Photometric Stereo by Differentiable Shadow Handling, Anisotropic Reflectance Modeling, and Neural Inverse Rendering](https://mlsanity.com/paper/2303.15101/) (2023)
- [Multi-view Surface Reconstruction Using Normal and Reflectance Cues](https://mlsanity.com/paper/2506.04115/) (2025)
- [Leveraging Spatial and Photometric Context for Calibrated Non-Lambertian Photometric Stereo](https://mlsanity.com/paper/2103.12106/) (2021)
- [Incorporating Lambertian Priors into Surface Normals Measurement](https://mlsanity.com/paper/2107.07192/) (2021)
- [Physics-Free Spectrally Multiplexed Photometric Stereo under Unknown Spectral Composition](https://mlsanity.com/paper/2410.20716/) (2024)
- [A Real-world Display Inverse Rendering Dataset](https://mlsanity.com/paper/2508.14411/) (2025)
- [MS-PS: A Multi-Scale Network for Photometric Stereo With a New Comprehensive Training Dataset](https://mlsanity.com/paper/2211.14118/) (2022)
- [Learning Efficient Photometric Feature Transform for Multi-view Stereo](https://mlsanity.com/paper/2103.14794/) (2021)
- [LUCES-MV: A Multi-View Dataset for Near-Field Point Light Source Photometric Stereo](https://mlsanity.com/paper/2412.16737/) (2024)

## Abstract (arXiv)

This paper addresses the problem of photometric stereo for non-Lambertian surfaces. Existing approaches often adopt simplified reflectance models to make the problem more tractable, but this greatly hinders their applications on real-world objects. In this paper, we propose a deep fully convolutional network, called PS-FCN, that takes an arbitrary number of images of a static object captured under different light directions with a fixed camera as input, and predicts a normal map of the object in a fast feed-forward pass. Unlike the recently proposed learning based method, PS-FCN does not require a pre-defined set of light directions during training and testing, and can handle multiple images and light directions in an order-agnostic manner. Although we train PS-FCN on synthetic data, it can generalize well on real datasets. We further show that PS-FCN can be easily extended to handle the problem of uncalibrated photometric stereo.Extensive experiments on public real datasets show that PS-FCN outperforms existing approaches in calibrated photometric stereo, and promising results are achieved in uncalibrated scenario, clearly demonstrating its effectiveness.