# Deep Graph Learning for Spatially-Varying Indoor Lighting Prediction

arXiv: https://arxiv.org/abs/2202.06300 · HTML: https://mlsanity.com/paper/2202.06300/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 47 references that are in the map.
- First posted on arXiv 13 Feb 2022.

## Builds on (most cited first, 28 of 28)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016)
- [Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1801.07455/) (2018)
- [DeepGCNs: Can GCNs Go as Deep as CNNs?](https://mlsanity.com/paper/1904.03751/) (2019)
- [Learning Convolutional Neural Networks for Graphs](https://mlsanity.com/paper/1605.05273/) (2016)
- [Graph R-CNN for Scene Graph Generation](https://mlsanity.com/paper/1808.00191/) (2018)
- [Diffusion-Convolutional Neural Networks](https://mlsanity.com/paper/1511.02136/) (2015)
- [Inverse Rendering for Complex Indoor Scenes: Shape, Spatially-Varying Lighting and SVBRDF from a Single Image](https://mlsanity.com/paper/1905.02722/) (2019)
- [HDR image reconstruction from a single exposure using deep CNNs](https://mlsanity.com/paper/1710.07480/) (2017)
- [Learning to Predict Indoor Illumination from a Single Image](https://mlsanity.com/paper/1704.00090/) (2017)
- [Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling](https://mlsanity.com/paper/1703.04826/) (2017)
- [Deep Parametric Indoor Lighting Estimation](https://mlsanity.com/paper/1910.08812/) (2019)
- [Factorizable Net: An Efficient Subgraph-based Framework for Scene Graph Generation](https://mlsanity.com/paper/1806.11538/) (2018)
- [Fast Spatially-Varying Indoor Lighting Estimation](https://mlsanity.com/paper/1906.03799/) (2019)
- [Rendering Synthetic Objects into Legacy Photographs](https://mlsanity.com/paper/1912.11565/) (2019)
- [Graph Convolutional Encoders for Syntax-aware Neural Machine Translation](https://mlsanity.com/paper/1704.04675/) (2017)
- [Deep Sky Modeling for Single Image Outdoor Lighting Estimation](https://mlsanity.com/paper/1905.03897/) (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)
- [Deep Outdoor Illumination Estimation](https://mlsanity.com/paper/1611.06403/) (2016)
- [EMLight: Lighting Estimation via Spherical Distribution Approximation](https://mlsanity.com/paper/2012.11116/) (2020)
- [All-Weather Deep Outdoor Lighting Estimation](https://mlsanity.com/paper/1906.04909/) (2019)
- [Intrinsic3D: High-Quality 3D Reconstruction by Joint Appearance and Geometry Optimization with Spatially-Varying Lighting](https://mlsanity.com/paper/1708.01670/) (2017)
- [Object-based Illumination Estimation with Rendering-aware Neural Networks](https://mlsanity.com/paper/2008.02514/) (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)

## Built on by (most cited first, 6 of 6)

- [Editable Indoor Lighting Estimation](https://mlsanity.com/paper/2211.03928/) (2022)
- [DiffusionLight-Turbo: Accelerated Light Probes for Free via Single-Pass Chrome Ball Inpainting](https://mlsanity.com/paper/2507.01305/) (2025)
- [Diffusion Reflectance Map: Single-Image Stochastic Inverse Rendering of Illumination and Reflectance](https://mlsanity.com/paper/2312.04529/) (2023)
- [360U-Former: HDR Illumination Estimation with Panoramic Adapted Vision Transformers](https://mlsanity.com/paper/2410.13566/) (2024)
- [HDR Environment Map Estimation with Latent Diffusion Models](https://mlsanity.com/paper/2507.21261/) (2025)
- [VQT-Light:Lightweight HDR Illumination Map Prediction with Richer Texture.pdf](https://mlsanity.com/paper/2509.12556/) (2025)

## Abstract (arXiv)

Lighting prediction from a single image is becoming increasingly important in many vision and augmented reality (AR) applications in which shading and shadow consistency between virtual and real objects should be guaranteed. However, this is a notoriously ill-posed problem, especially for indoor scenarios, because of the complexity of indoor luminaires and the limited information involved in 2D images. In this paper, we propose a graph learning-based framework for indoor lighting estimation. At its core is a new lighting model (dubbed DSGLight) based on depth-augmented Spherical Gaussians (SG) and a Graph Convolutional Network (GCN) that infers the new lighting representation from a single LDR image of limited field-of-view. Our lighting model builds 128 evenly distributed SGs over the indoor panorama, where each SG encoding the lighting and the depth around that node. The proposed GCN then learns the mapping from the input image to DSGLight. Compared with existing lighting models, our DSGLight encodes both direct lighting and indirect environmental lighting more faithfully and compactly. It also makes network training and inference more stable. The estimated depth distribution enables temporally stable shading and shadows under spatially-varying lighting. Through thorough experiments, we show that our method obviously outperforms existing methods both qualitatively and quantitatively.