# Invariant Descriptors for Intrinsic Reflectance Optimization

arXiv: https://arxiv.org/abs/2204.04076 · HTML: https://mlsanity.com/paper/2204.04076/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 53 references that are in the map.
- First posted on arXiv 8 Apr 2022.

## Builds on (most cited first, 12 of 12)

- [Stacked Conditional Generative Adversarial Networks for Jointly Learning Shadow Detection and Shadow Removal](https://mlsanity.com/paper/1712.02478/) (2017)
- [CGIntrinsics: Better Intrinsic Image Decomposition through Physically-Based Rendering](https://mlsanity.com/paper/1808.08601/) (2018)
- [Learning Intrinsic Image Decomposition from Watching the World](https://mlsanity.com/paper/1804.00582/) (2018)
- [Unsupervised Learning for Intrinsic Image Decomposition from a Single Image](https://mlsanity.com/paper/1911.09930/) (2019)
- [Learning Non-Lambertian Object Intrinsics across ShapeNet Categories](https://mlsanity.com/paper/1612.08510/) (2016)
- [STAR: A Structure and Texture Aware Retinex Model](https://mlsanity.com/paper/1906.06690/) (2019)
- [Direct Intrinsics: Learning Albedo-Shading Decomposition by Convolutional Regression](https://mlsanity.com/paper/1512.02311/) (2015)
- [Joint Learning of Intrinsic Images and Semantic Segmentation](https://mlsanity.com/paper/1807.11857/) (2018)
- [CNN based Learning using Reflection and Retinex Models for Intrinsic Image Decomposition](https://mlsanity.com/paper/1712.01056/) (2017)
- [Reflectance Adaptive Filtering Improves Intrinsic Image Estimation](https://mlsanity.com/paper/1612.05062/) (2016)
- [ShadingNet: Image Intrinsics by Fine-Grained Shading Decomposition](https://mlsanity.com/paper/1912.04023/) (2019)
- [Deep intrinsic decomposition trained on surreal scenes yet with realistic light effects](https://mlsanity.com/paper/2009.06295/) (2020)

## Abstract (arXiv)

Intrinsic image decomposition aims to factorize an image into albedo (reflectance) and shading (illumination) sub-components. Being ill-posed and under-constrained, it is a very challenging computer vision problem. There are infinite pairs of reflectance and shading images that can reconstruct the same input. To address the problem, Intrinsic Images in the Wild provides an optimization framework based on a dense conditional random field (CRF) formulation that considers long-range material relations. We improve upon their model by introducing illumination invariant image descriptors: color ratios. The color ratios and the reflectance intrinsic are both invariant to illumination and thus are highly correlated. Through detailed experiments, we provide ways to inject the color ratios into the dense CRF optimization. Our approach is physics-based, learning-free and leads to more accurate and robust reflectance decompositions.