# A Dataset of Multi-Illumination Images in the Wild

arXiv: https://arxiv.org/abs/1910.08131 · HTML: https://mlsanity.com/paper/1910.08131/

## Key facts

- Cited by 45 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Oct 2019.

## Built on by (most cited first, 30 of 45)

- [Making Images Real Again: A Comprehensive Survey on Deep Image Composition](https://mlsanity.com/paper/2106.14490/) (2021)
- [Free-viewpoint Indoor Neural Relighting from Multi-view Stereo](https://mlsanity.com/paper/2106.13299/) (2021)
- [LightLab: Controlling Light Sources in Images with Diffusion Models](https://mlsanity.com/paper/2505.09608/) (2025)
- [OpenIllumination: A Multi-Illumination Dataset for Inverse Rendering Evaluation on Real Objects](https://mlsanity.com/paper/2309.07921/) (2023)
- [GMLight: Lighting Estimation via Geometric Distribution Approximation](https://mlsanity.com/paper/2102.10244/) (2021)
- [Materialistic: Selecting Similar Materials in Images](https://mlsanity.com/paper/2305.13291/) (2023)
- [A Dense Material Segmentation Dataset for Indoor and Outdoor Scene Parsing](https://mlsanity.com/paper/2207.10614/) (2022)
- [Auto White-Balance Correction for Mixed-Illuminant Scenes](https://mlsanity.com/paper/2109.08750/) (2021)
- [Sparse Needlets for Lighting Estimation with Spherical Transport Loss](https://mlsanity.com/paper/2106.13090/) (2021)
- [Objects With Lighting: A Real-World Dataset for Evaluating Reconstruction and Rendering for Object Relighting](https://mlsanity.com/paper/2401.09126/) (2024)
- [Plan2Scene: Converting Floorplans to 3D Scenes](https://mlsanity.com/paper/2106.05375/) (2021)
- [Latent Intrinsics Emerge from Training to Relight](https://mlsanity.com/paper/2405.21074/) (2024)
- [IntrinsicEdit: Precise generative image manipulation in intrinsic space](https://mlsanity.com/paper/2505.08889/) (2025)
- [SAMa: Material-aware 3D Selection and Segmentation](https://mlsanity.com/paper/2411.19322/) (2024)
- [Attentive Illumination Decomposition Model for Multi-Illuminant White Balancing](https://mlsanity.com/paper/2402.18277/) (2024)
- [Lasagna: Layered Score Distillation for Disentangled Object Relighting](https://mlsanity.com/paper/2312.00833/) (2023)
- [Relighting from a Single Image: Datasets and Deep Intrinsic-based Architecture](https://mlsanity.com/paper/2409.18770/) (2024)
- [DSRN: an Efficient Deep Network for Image Relighting](https://mlsanity.com/paper/2102.09242/) (2021)
- [Towards High-Quality Specular Highlight Removal by Leveraging Large-Scale Synthetic Data](https://mlsanity.com/paper/2309.06302/) (2023)
- [Computational Flash Photography through Intrinsics](https://mlsanity.com/paper/2306.06089/) (2023)
- [After the Party: Navigating the Mapping From Color to Ambient Lighting](https://mlsanity.com/paper/2508.02168/) (2025)
- [LightCity: An Urban Dataset for Outdoor Inverse Rendering and Reconstruction under Multi-illumination Conditions](https://mlsanity.com/paper/2602.01118/) (2026)
- [Designing An Illumination-Aware Network for Deep Image Relighting](https://mlsanity.com/paper/2207.10582/) (2022)
- [Fine-Grained Spatially Varying Material Selection in Images](https://mlsanity.com/paper/2506.09023/) (2025)
- [Deep Portrait Lighting Enhancement with 3D Guidance](https://mlsanity.com/paper/2108.02121/) (2021)
- [MLI-NeRF: Multi-Light Intrinsic-Aware Neural Radiance Fields](https://mlsanity.com/paper/2411.17235/) (2024)
- [UAVLight: A Benchmark for Illumination-Robust 3D Reconstruction in Unmanned Aerial Vehicle (UAV) Scenes](https://mlsanity.com/paper/2511.21565/) (2025)
- [ICTPolarReal: A Polarized Reflection and Material Dataset of Real World Objects](https://mlsanity.com/paper/2603.24912/) (2026)
- [Video Generation Models Are Inherent Lighting Estimators](https://mlsanity.com/paper/2607.04674/) (2026)
- [Shedding Light: A Benchmark for Evaluating Lighting Understanding in Generative Image Models](https://mlsanity.com/paper/2609.10787/) (2026)

## Abstract (arXiv)

Collections of images under a single, uncontrolled illumination have enabled the rapid advancement of core computer vision tasks like classification, detection, and segmentation. But even with modern learning techniques, many inverse problems involving lighting and material understanding remain too severely ill-posed to be solved with single-illumination datasets. To fill this gap, we introduce a new multi-illumination dataset of more than 1000 real scenes, each captured under 25 lighting conditions. We demonstrate the richness of this dataset by training state-of-the-art models for three challenging applications: single-image illumination estimation, image relighting, and mixed-illuminant white balance.