# Discontinuity-aware Normal Integration for Generic Central Camera Models

arXiv: https://arxiv.org/abs/2507.06075 · HTML: https://mlsanity.com/paper/2507.06075/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 35 references that are in the map.
- First posted on arXiv 8 Jul 2025.

## Builds on (most cited first, 8 of 8)

- [Rethinking Inductive Biases for Surface Normal Estimation](https://mlsanity.com/paper/2403.00712/) (2024)
- [Multi-View Photometric Stereo: A Robust Solution and Benchmark Dataset for Spatially Varying Isotropic Materials](https://mlsanity.com/paper/2001.06659/) (2020)
- [Scalable, Detailed and Mask-Free Universal Photometric Stereo](https://mlsanity.com/paper/2303.15724/) (2023)
- [Normal Integration: A Survey](https://mlsanity.com/paper/1709.05940/) (2017)
- [An Adaptive Screen-Space Meshing Approach for Normal Integration](https://mlsanity.com/paper/2409.16907/) (2024)
- [Variational Methods for Normal Integration](https://mlsanity.com/paper/1709.05965/) (2017)
- [Least squares surface reconstruction on arbitrary domains](https://mlsanity.com/paper/2007.08661/) (2020)
- [Discontinuity-preserving Normal Integration with Auxiliary Edges](https://mlsanity.com/paper/2404.03138/) (2024)

## Built on by (most cited first, 1 of 1)

- [Towards Fast and Scalable Normal Integration using Continuous Components](https://mlsanity.com/paper/2510.11508/) (2025)

## Abstract (arXiv)

Recovering a 3D surface from its surface normal map, a problem known as normal integration, is a key component for photometric shape reconstruction techniques such as shape-from-shading and photometric stereo. The vast majority of existing approaches for normal integration handle only implicitly the presence of depth discontinuities and are limited to orthographic or ideal pinhole cameras. In this paper, we propose a novel formulation that allows modeling discontinuities explicitly and handling generic central cameras. Our key idea is based on a local planarity assumption, that we model through constraints between surface normals and ray directions. Compared to existing methods, our approach more accurately approximates the relation between depth and surface normals, achieves state-of-the-art results on the standard normal integration benchmark, and is the first to directly handle generic central camera models.