# Learning Data-driven Reflectance Priors for Intrinsic Image Decomposition

arXiv: https://arxiv.org/abs/1510.02413 · HTML: https://mlsanity.com/paper/1510.02413/

## Key facts

- Cited by 24 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 8 Oct 2015.

## Built on by (most cited first, 24 of 24)

- [Intrinsic Image Decomposition via Ordinal Shading](https://mlsanity.com/paper/2311.12792/) (2023)
- [A Survey on Intrinsic Images: Delving Deep Into Lambert and Beyond](https://mlsanity.com/paper/2112.03842/) (2021)
- [PIE-Net: Photometric Invariant Edge Guided Network for Intrinsic Image Decomposition](https://mlsanity.com/paper/2203.16670/) (2022)
- [UrbanIR: Large-Scale Urban Scene Inverse Rendering from a Single Video](https://mlsanity.com/paper/2306.09349/) (2023)
- [Measured Albedo in the Wild: Filling the Gap in Intrinsics Evaluation](https://mlsanity.com/paper/2306.15662/) (2023)
- [ZeroComp: Zero-shot Object Compositing from Image Intrinsics via Diffusion](https://mlsanity.com/paper/2410.08168/) (2024)
- [Ouroboros: Single-step Diffusion Models for Cycle-consistent Forward and Inverse Rendering](https://mlsanity.com/paper/2508.14461/) (2025)
- [ScaleDepth: Decomposing Metric Depth Estimation into Scale Prediction and Relative Depth Estimation](https://mlsanity.com/paper/2407.08187/) (2024)
- [PRISM: A Unified Framework for Photorealistic Reconstruction and Intrinsic Scene Modeling](https://mlsanity.com/paper/2504.14219/) (2025)
- [Deep Monocular 3D Human Pose Estimation via Cascaded Dimension-Lifting](https://mlsanity.com/paper/2104.03520/) (2021)
- [IBL-NeRF: Image-Based Lighting Formulation of Neural Radiance Fields](https://mlsanity.com/paper/2210.08202/) (2022)
- [DeRenderNet: Intrinsic Image Decomposition of Urban Scenes with Shape-(In)dependent Shading Rendering](https://mlsanity.com/paper/2104.13602/) (2021)
- [Unsupervised Intrinsic Image Decomposition with LiDAR Intensity](https://mlsanity.com/paper/2303.10820/) (2023)
- [Structure from Duplicates: Neural Inverse Graphics from a Pile of Objects](https://mlsanity.com/paper/2401.05236/) (2024)
- [SILT: Self-supervised Lighting Transfer Using Implicit Image Decomposition](https://mlsanity.com/paper/2110.12914/) (2021)
- [JoIN: Joint GANs Inversion for Intrinsic Image Decomposition](https://mlsanity.com/paper/2305.11321/) (2023)
- [DPF: Learning Dense Prediction Fields with Weak Supervision](https://mlsanity.com/paper/2303.16890/) (2023)
- [Discriminative feature encoding for intrinsic image decomposition](https://mlsanity.com/paper/2209.12155/) (2022)
- [ICTPolarReal: A Polarized Reflection and Material Dataset of Real World Objects](https://mlsanity.com/paper/2603.24912/) (2026)
- [Self-Supervised Intrinsic Image Decomposition Network Considering Reflectance Consistency](https://mlsanity.com/paper/2111.04506/) (2021)
- [Intrinsic Image Decomposition Using Point Cloud Representation](https://mlsanity.com/paper/2307.10924/) (2023)
- [Unsupervised Intrinsic Image Decomposition with LiDAR Intensity Enhanced Training](https://mlsanity.com/paper/2403.14089/) (2024)
- [Texture-aware Intrinsic Image Decomposition with Model- and Learning-based Priors](https://mlsanity.com/paper/2509.09352/) (2025)
- [IPCD: Intrinsic Point-Cloud Decomposition](https://mlsanity.com/paper/2511.09866/) (2025)

## Abstract (arXiv)

We propose a data-driven approach for intrinsic image decomposition, which is the process of inferring the confounding factors of reflectance and shading in an image. We pose this as a two-stage learning problem. First, we train a model to predict relative reflectance ordering between image patches (`brighter', `darker', `same') from large-scale human annotations, producing a data-driven reflectance prior. Second, we show how to naturally integrate this learned prior into existing energy minimization frameworks for intrinsic image decomposition. We compare our method to the state-of-the-art approach of Bell et al. on both decomposition and image relighting tasks, demonstrating the benefits of the simple relative reflectance prior, especially for scenes under challenging lighting conditions.