# ShadingNet: Image Intrinsics by Fine-Grained Shading Decomposition

arXiv: https://arxiv.org/abs/1912.04023 · HTML: https://mlsanity.com/paper/1912.04023/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Dec 2019.

## Built on by (most cited first, 12 of 12)

- [LumiNet: Latent Intrinsics Meets Diffusion Models for Indoor Scene Relighting](https://mlsanity.com/paper/2412.00177/) (2024)
- [PIE-Net: Photometric Invariant Edge Guided Network for Intrinsic Image Decomposition](https://mlsanity.com/paper/2203.16670/) (2022)
- [Estimating Reflectance Layer from A Single Image: Integrating Reflectance Guidance and Shadow/Specular Aware Learning](https://mlsanity.com/paper/2211.14751/) (2022)
- [Retinex-Diffusion: On Controlling Illumination Conditions in Diffusion Models via Retinex Theory](https://mlsanity.com/paper/2407.20785/) (2024)
- [Specularity Factorization for Low-Light Enhancement](https://mlsanity.com/paper/2404.01998/) (2024)
- [DPF: Learning Dense Prediction Fields with Weak Supervision](https://mlsanity.com/paper/2303.16890/) (2023)
- [SIGNet: Intrinsic Image Decomposition by a Semantic and Invariant Gradient Driven Network for Indoor Scenes](https://mlsanity.com/paper/2208.14369/) (2022)
- [Discriminative feature encoding for intrinsic image decomposition](https://mlsanity.com/paper/2209.12155/) (2022)
- [Invariant Descriptors for Intrinsic Reflectance Optimization](https://mlsanity.com/paper/2204.04076/) (2022)
- [Intrinsic Image Decomposition Using Point Cloud Representation](https://mlsanity.com/paper/2307.10924/) (2023)
- [Relighting as a Probe of Visual Priors via Augmented Latent Intrinsics](https://mlsanity.com/paper/2602.01391/) (2026)
- [Olbedo: An Albedo and Shading Aerial Dataset for Large-Scale Outdoor Environments](https://mlsanity.com/paper/2602.22025/) (2026)

## Abstract (arXiv)

In general, intrinsic image decomposition algorithms interpret shading as one unified component including all photometric effects. As shading transitions are generally smoother than reflectance (albedo) changes, these methods may fail in distinguishing strong photometric effects from reflectance variations. Therefore, in this paper, we propose to decompose the shading component into direct (illumination) and indirect shading (ambient light and shadows) subcomponents. The aim is to distinguish strong photometric effects from reflectance variations. An end-to-end deep convolutional neural network (ShadingNet) is proposed that operates in a fine-to-coarse manner with a specialized fusion and refinement unit exploiting the fine-grained shading model. It is designed to learn specific reflectance cues separated from specific photometric effects to analyze the disentanglement capability. A large-scale dataset of scene-level synthetic images of outdoor natural environments is provided with fine-grained intrinsic image ground-truths. Large scale experiments show that our approach using fine-grained shading decompositions outperforms state-of-the-art algorithms utilizing unified shading on NED, MPI Sintel, GTA V, IIW, MIT Intrinsic Images, 3DRMS and SRD datasets.