# Self-calibrating Deep Photometric Stereo Networks

arXiv: https://arxiv.org/abs/1903.07366 · HTML: https://mlsanity.com/paper/1903.07366/

## Key facts

- Cited by 47 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Mar 2019.

## Built on by (most cited first, 30 of 47)

- [PS-NeRF: Neural Inverse Rendering for Multi-view Photometric Stereo](https://mlsanity.com/paper/2207.11406/) (2022)
- [MeshFormer: High-Quality Mesh Generation with 3D-Guided Reconstruction Model](https://mlsanity.com/paper/2408.10198/) (2024)
- [Shape from Polarization for Complex Scenes in the Wild](https://mlsanity.com/paper/2112.11377/) (2021)
- [Shape and Material Capture at Home](https://mlsanity.com/paper/2104.06397/) (2021)
- [Robust Reflection Removal with Reflection-free Flash-only Cues](https://mlsanity.com/paper/2103.04273/) (2021)
- [Uncertainty-Aware Deep Multi-View Photometric Stereo](https://mlsanity.com/paper/2202.13071/) (2022)
- [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 Radiance Fields Approach to Deep Multi-View Photometric Stereo](https://mlsanity.com/paper/2110.05594/) (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)
- [SuperNormal: Neural Surface Reconstruction via Multi-View Normal Integration](https://mlsanity.com/paper/2312.04803/) (2023)
- [Self-calibrating Photometric Stereo by Neural Inverse Rendering](https://mlsanity.com/paper/2207.07815/) (2022)
- [Multi-View Photometric Stereo Revisited](https://mlsanity.com/paper/2210.07670/) (2022)
- [ShadowNeuS: Neural SDF Reconstruction by Shadow Ray Supervision](https://mlsanity.com/paper/2211.14086/) (2022)
- [Multi-View Azimuth Stereo via Tangent Space Consistency](https://mlsanity.com/paper/2303.16447/) (2023)
- [PS-Transformer: Learning Sparse Photometric Stereo Network using Self-Attention Mechanism](https://mlsanity.com/paper/2211.11386/) (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)
- [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)
- [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)
- [Sparse Views, Near Light: A Practical Paradigm for Uncalibrated Point-light Photometric Stereo](https://mlsanity.com/paper/2404.00098/) (2024)
- [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)
- [Incorporating Lambertian Priors into Surface Normals Measurement](https://mlsanity.com/paper/2107.07192/) (2021)
- [NPLMV-PS: Neural Point-Light Multi-View Photometric Stereo](https://mlsanity.com/paper/2405.12057/) (2024)

## Abstract (arXiv)

This paper proposes an uncalibrated photometric stereo method for non-Lambertian scenes based on deep learning. Unlike previous approaches that heavily rely on assumptions of specific reflectances and light source distributions, our method is able to determine both shape and light directions of a scene with unknown arbitrary reflectances observed under unknown varying light directions. To achieve this goal, we propose a two-stage deep learning architecture, called SDPS-Net, which can effectively take advantage of intermediate supervision, resulting in reduced learning difficulty compared to a single-stage model. Experiments on both synthetic and real datasets show that our proposed approach significantly outperforms previous uncalibrated photometric stereo methods.