LED-based Photometric Stereo: Modeling, Calibration and Numerical Solution
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Abstract (arXiv)
We conduct a thorough study of photometric stereo under nearby point light source illumination, from modeling to numerical solution, through calibration. In the classical formulation of photometric stereo, the luminous fluxes are assumed to be directional, which is very difficult to achieve in practice. Rather, we use light-emitting diodes (LEDs) to illuminate the scene to reconstruct. Such point light sources are very convenient to use, yet they yield a more complex photometric stereo model which is arduous to solve. We first derive in a physically sound manner this model, and show how to calibrate its parameters. Then, we discuss two state-of-the-art numerical solutions. The first one alternatingly estimates the albedo and the normals, and then integrates the normals into a depth map. It is shown empirically to be independent from the initialization, but convergence of this sequential approach is not established. The second one directly recovers the depth, by formulating photometric stereo as a system of PDEs which are partially linearized using image ratios. Although the sequential approach is avoided, initialization matters a lot and convergence is not established either. Therefore, we introduce a provably convergent alternating reweighted least-squares scheme for solving the original system of PDEs, without resorting to image ratios for linearization. Finally, we extend this study to the case of RGB images.
Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Jul 2017.
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Citing papers per year: 2021: 1; 2022: 5; 2023: 1; 2024: 2; 2025: 0; 2026: 3.
12 papers.
- 2022S3-NeRF: Neural Reflectance Field from Shading and Shadow under a Single Viewpointcited by 30
- 2021LUCES: A Dataset for Near-Field Point Light Source Photometric Stereocited by 15
- 2022Fast Light-Weight Near-Field Photometric Stereocited by 12
- 2022A CNN Based Approach for the Point-Light Photometric Stereo Problemcited by 10
- 2022Edge-preserving Near-light Photometric Stereo with Neural Surfacescited by 6
- 2022High-Quality RGB-D Reconstruction via Multi-View Uncalibrated Photometric Stereo and Gradient-SDFcited by 5
- 2024Sparse Views, Near Light: A Practical Paradigm for Uncalibrated Point-light Photometric Stereocited by 5
- 2023Towards Scalable Multi-View Reconstruction of Geometry and Materialscited by 3
- 2024LUCES-MV: A Multi-View Dataset for Near-Field Point Light Source Photometric Stereocited by 2
- 2026NLiPsCalib: An Efficient Calibration Framework for High-Fidelity 3D Reconstruction of Curved Visuotactile Sensorscited by 2
- 2026Near-Light Color Photometric Stereo for Mono-Chromatic Non-Lambertian Surfacescited by 0
- 2026Near-light Photometric Stereo with Symmetric Lightscited by 0
- 2026Near-light Photometric Stereo with Symmetric Lights17 Mar 2026
- 2026NLiPsCalib: An Efficient Calibration Framework for High-Fidelity 3D Reconstruction of Curved Visuotactile Sensors10 Mar 2026
- 2026Near-Light Color Photometric Stereo for Mono-Chromatic Non-Lambertian Surfaces19 Jan 2026
- 2024LUCES-MV: A Multi-View Dataset for Near-Field Point Light Source Photometric Stereo21 Dec 2024
- 2024Sparse Views, Near Light: A Practical Paradigm for Uncalibrated Point-light Photometric Stereo29 Mar 2024
- 2023Towards Scalable Multi-View Reconstruction of Geometry and Materials6 Jun 2023
- 2022High-Quality RGB-D Reconstruction via Multi-View Uncalibrated Photometric Stereo and Gradient-SDF21 Oct 2022
- 2022S3-NeRF: Neural Reflectance Field from Shading and Shadow under a Single Viewpoint17 Oct 2022
- 2022A CNN Based Approach for the Point-Light Photometric Stereo Problem10 Oct 2022
- 2022Edge-preserving Near-light Photometric Stereo with Neural Surfaces11 Jul 2022
- 2022Fast Light-Weight Near-Field Photometric Stereo30 Mar 2022
- 2021LUCES: A Dataset for Near-Field Point Light Source Photometric Stereo27 Apr 2021