# Edge-preserving Near-light Photometric Stereo with Neural Surfaces

arXiv: https://arxiv.org/abs/2207.04622 · HTML: https://mlsanity.com/paper/2207.04622/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 38 references that are in the map.
- First posted on arXiv 11 Jul 2022.

## Builds on (most cited first, 11 of 11)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation](https://mlsanity.com/paper/1901.05103/) (2019)
- [Implicit Neural Representations with Periodic Activation Functions](https://mlsanity.com/paper/2006.09661/) (2020)
- [PhySG: Inverse Rendering with Spherical Gaussians for Physics-based Material Editing and Relighting](https://mlsanity.com/paper/2104.00674/) (2021)
- [DISN: Deep Implicit Surface Network for High-quality Single-view 3D Reconstruction](https://mlsanity.com/paper/1905.10711/) (2019)
- [NeRFactor: Neural Factorization of Shape and Reflectance Under an Unknown Illumination](https://mlsanity.com/paper/2106.01970/) (2021)
- [Neural Inverse Rendering for General Reflectance Photometric Stereo](https://mlsanity.com/paper/1802.10328/) (2018)
- [Normal Integration: A Survey](https://mlsanity.com/paper/1709.05940/) (2017)
- [LUCES: A Dataset for Near-Field Point Light Source Photometric Stereo](https://mlsanity.com/paper/2104.13135/) (2021)
- [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)

## Built on by (most cited first, 6 of 6)

- [Deep Learning Methods for Calibrated Photometric Stereo and Beyond](https://mlsanity.com/paper/2212.08414/) (2022)
- [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)
- [A Neural Height-Map Approach for the Binocular Photometric Stereo Problem](https://mlsanity.com/paper/2311.05958/) (2023)
- [PIR: Photometric Inverse Rendering with Shading Cues Modeling and Surface Reflectance Regularization](https://mlsanity.com/paper/2408.06828/) (2024)
- [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 presents a near-light photometric stereo method that faithfully preserves sharp depth edges in the 3D reconstruction. Unlike previous methods that rely on finite differentiation for approximating depth partial derivatives and surface normals, we introduce an analytically differentiable neural surface in near-light photometric stereo for avoiding differentiation errors at sharp depth edges, where the depth is represented as a neural function of the image coordinates. By further formulating the Lambertian albedo as a dependent variable resulting from the surface normal and depth, our method is insusceptible to inaccurate depth initialization. Experiments on both synthetic and real-world scenes demonstrate the effectiveness of our method for detailed shape recovery with edge preservation.