# Editable Indoor Lighting Estimation

arXiv: https://arxiv.org/abs/2211.03928 · HTML: https://mlsanity.com/paper/2211.03928/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 35 references that are in the map.
- First posted on arXiv 8 Nov 2022.

## Builds on (most cited first, 27 of 27)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs](https://mlsanity.com/paper/1711.11585/) (2017)
- [StarGAN v2: Diverse Image Synthesis for Multiple Domains](https://mlsanity.com/paper/1912.01865/) (2019)
- [Contrastive Learning for Unpaired Image-to-Image Translation](https://mlsanity.com/paper/2007.15651/) (2020)
- [Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional Architecture](https://mlsanity.com/paper/1411.4734/) (2014)
- [SynSin: End-to-end View Synthesis from a Single Image](https://mlsanity.com/paper/1912.08804/) (2019)
- [Inverse Rendering for Complex Indoor Scenes: Shape, Spatially-Varying Lighting and SVBRDF from a Single Image](https://mlsanity.com/paper/1905.02722/) (2019)
- [Learning to Predict Indoor Illumination from a Single Image](https://mlsanity.com/paper/1704.00090/) (2017)
- [Neural Inverse Rendering of an Indoor Scene from a Single Image](https://mlsanity.com/paper/1901.02453/) (2019)
- [LayoutNet: Reconstructing the 3D Room Layout from a Single RGB Image](https://mlsanity.com/paper/1803.08999/) (2018)
- [Deep Parametric Indoor Lighting Estimation](https://mlsanity.com/paper/1910.08812/) (2019)
- [Learning Indoor Inverse Rendering with 3D Spatially-Varying Lighting](https://mlsanity.com/paper/2109.06061/) (2021)
- [Fast Spatially-Varying Indoor Lighting Estimation](https://mlsanity.com/paper/1906.03799/) (2019)
- [DeepLight: Learning Illumination for Unconstrained Mobile Mixed Reality](https://mlsanity.com/paper/1904.01175/) (2019)
- [Lighthouse: Predicting Lighting Volumes for Spatially-Coherent Illumination](https://mlsanity.com/paper/2003.08367/) (2020)
- [Neural Illumination: Lighting Prediction for Indoor Environments](https://mlsanity.com/paper/1906.07370/) (2019)
- [Corners for Layout: End-to-End Layout Recovery from 360 Images](https://mlsanity.com/paper/1903.08094/) (2019)
- [EMLight: Lighting Estimation via Spherical Distribution Approximation](https://mlsanity.com/paper/2012.11116/) (2020)
- [HDR Environment Map Estimation for Real-Time Augmented Reality](https://mlsanity.com/paper/2011.10687/) (2020)
- [RoomNet: End-to-End Room Layout Estimation](https://mlsanity.com/paper/1703.06241/) (2017)
- [GMLight: Lighting Estimation via Geometric Distribution Approximation](https://mlsanity.com/paper/2102.10244/) (2021)
- [PointAR: Efficient Lighting Estimation for Mobile Augmented Reality](https://mlsanity.com/paper/2004.00006/) (2020)
- [Sparse Needlets for Lighting Estimation with Spherical Transport Loss](https://mlsanity.com/paper/2106.13090/) (2021)
- [Learning to Estimate Indoor Lighting from 3D Objects](https://mlsanity.com/paper/1806.03994/) (2018)
- [Learning to Reconstruct 3D Non-Cuboid Room Layout from a Single RGB Image](https://mlsanity.com/paper/2104.07986/) (2021)
- [Deep Graph Learning for Spatially-Varying Indoor Lighting Prediction](https://mlsanity.com/paper/2202.06300/) (2022)

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

- [DiffusionLight: Light Probes for Free by Painting a Chrome Ball](https://mlsanity.com/paper/2312.09168/) (2023)
- [EverLight: Indoor-Outdoor Editable HDR Lighting Estimation](https://mlsanity.com/paper/2304.13207/) (2023)
- [Latent Intrinsics Emerge from Training to Relight](https://mlsanity.com/paper/2405.21074/) (2024)
- [ZeroComp: Zero-shot Object Compositing from Image Intrinsics via Diffusion](https://mlsanity.com/paper/2410.08168/) (2024)
- [LuxDiT: Lighting Estimation with Video Diffusion Transformer](https://mlsanity.com/paper/2509.03680/) (2025)
- [DiffusionLight-Turbo: Accelerated Light Probes for Free via Single-Pass Chrome Ball Inpainting](https://mlsanity.com/paper/2507.01305/) (2025)
- [Towards a Perceptual Evaluation Framework for Lighting Estimation](https://mlsanity.com/paper/2312.04334/) (2023)
- [MAIR++: Improving Multi-view Attention Inverse Rendering with Implicit Lighting Representation](https://mlsanity.com/paper/2408.06707/) (2024)
- [360U-Former: HDR Illumination Estimation with Panoramic Adapted Vision Transformers](https://mlsanity.com/paper/2410.13566/) (2024)
- [ESR-NeRF: Emissive Source Reconstruction Using LDR Multi-view Images](https://mlsanity.com/paper/2404.15707/) (2024)
- [HDR Environment Map Estimation with Latent Diffusion Models](https://mlsanity.com/paper/2507.21261/) (2025)
- [Improving the color accuracy of lighting estimation models](https://mlsanity.com/paper/2509.18390/) (2025)

## Abstract (arXiv)

We present a method for estimating lighting from a single perspective image of an indoor scene. Previous methods for predicting indoor illumination usually focus on either simple, parametric lighting that lack realism, or on richer representations that are difficult or even impossible to understand or modify after prediction. We propose a pipeline that estimates a parametric light that is easy to edit and allows renderings with strong shadows, alongside with a non-parametric texture with high-frequency information necessary for realistic rendering of specular objects. Once estimated, the predictions obtained with our model are interpretable and can easily be modified by an artist/user with a few mouse clicks. Quantitative and qualitative results show that our approach makes indoor lighting estimation easier to handle by a casual user, while still producing competitive results.