# All-Weather Deep Outdoor Lighting Estimation

arXiv: https://arxiv.org/abs/1906.04909 · HTML: https://mlsanity.com/paper/1906.04909/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2019.

## Built on by (most cited first, 30 of 41)

- [Editable Scene Simulation for Autonomous Driving via Collaborative LLM-Agents](https://mlsanity.com/paper/2402.05746/) (2024)
- [Neural Fields meet Explicit Geometric Representation for Inverse Rendering of Urban Scenes](https://mlsanity.com/paper/2304.03266/) (2023)
- [Learning Indoor Inverse Rendering with 3D Spatially-Varying Lighting](https://mlsanity.com/paper/2109.06061/) (2021)
- [Making Images Real Again: A Comprehensive Survey on Deep Image Composition](https://mlsanity.com/paper/2106.14490/) (2021)
- [Deep Learning for HDR Imaging: State-of-the-Art and Future Trends](https://mlsanity.com/paper/2110.10394/) (2021)
- [Shadow Generation for Composite Image in Real-world Scenes](https://mlsanity.com/paper/2104.10338/) (2021)
- [Neural Light Field Estimation for Street Scenes with Differentiable Virtual Object Insertion](https://mlsanity.com/paper/2208.09480/) (2022)
- [EverLight: Indoor-Outdoor Editable HDR Lighting Estimation](https://mlsanity.com/paper/2304.13207/) (2023)
- [OutCast: Outdoor Single-image Relighting with Cast Shadows](https://mlsanity.com/paper/2204.09341/) (2022)
- [A Survey of Representation Learning, Optimization Strategies, and Applications for Omnidirectional Vision](https://mlsanity.com/paper/2502.10444/) (2025)
- [UrbanIR: Large-Scale Urban Scene Inverse Rendering from a Single Video](https://mlsanity.com/paper/2306.09349/) (2023)
- [LightSim: Neural Lighting Simulation for Urban Scenes](https://mlsanity.com/paper/2312.06654/) (2023)
- [IRIS: Inverse Rendering of Indoor Scenes from Low Dynamic Range Images](https://mlsanity.com/paper/2401.12977/) (2024)
- [Sparse Needlets for Lighting Estimation with Spherical Transport Loss](https://mlsanity.com/paper/2106.13090/) (2021)
- [Shadow Generation for Composite Image Using Diffusion model](https://mlsanity.com/paper/2403.15234/) (2024)
- [Spatially-Varying Outdoor Lighting Estimation from Intrinsics](https://mlsanity.com/paper/2104.04160/) (2021)
- [Intrinsic Harmonization for Illumination-Aware Compositing](https://mlsanity.com/paper/2312.03698/) (2023)
- [nLMVS-Net: Deep Non-Lambertian Multi-View Stereo](https://mlsanity.com/paper/2207.11876/) (2022)
- [GeoFill: Reference-Based Image Inpainting with Better Geometric Understanding](https://mlsanity.com/paper/2201.08131/) (2022)
- [Seeing through Satellite Images at Street Views](https://mlsanity.com/paper/2505.17001/) (2025)
- [Deep Graph Learning for Spatially-Varying Indoor Lighting Prediction](https://mlsanity.com/paper/2202.06300/) (2022)
- [Foreground Object Search by Distilling Composite Image Feature](https://mlsanity.com/paper/2308.04990/) (2023)
- [Towards a Perceptual Evaluation Framework for Lighting Estimation](https://mlsanity.com/paper/2312.04334/) (2023)
- [Scene-Conditional 3D Object Stylization and Composition](https://mlsanity.com/paper/2312.12419/) (2023)
- [UniLight: A Unified Representation for Lighting](https://mlsanity.com/paper/2512.04267/) (2025)
- [360U-Former: HDR Illumination Estimation with Panoramic Adapted Vision Transformers](https://mlsanity.com/paper/2410.13566/) (2024)
- [Spatio-Temporal Outdoor Lighting Aggregation on Image Sequences using Transformer Networks](https://mlsanity.com/paper/2202.09206/) (2022)
- [Lighting in Motion: Spatiotemporal HDR Lighting Estimation](https://mlsanity.com/paper/2512.13597/) (2025)
- [Spatially and color consistent environment lighting estimation using deep neural networks for mixed reality](https://mlsanity.com/paper/2108.07903/) (2021)
- [Pop-Out Motion: 3D-Aware Image Deformation via Learning the Shape Laplacian](https://mlsanity.com/paper/2203.15235/) (2022)

## Abstract (arXiv)

We present a neural network that predicts HDR outdoor illumination from a single LDR image. At the heart of our work is a method to accurately learn HDR lighting from LDR panoramas under any weather condition. We achieve this by training another CNN (on a combination of synthetic and real images) to take as input an LDR panorama, and regress the parameters of the Lalonde-Matthews outdoor illumination model. This model is trained such that it a) reconstructs the appearance of the sky, and b) renders the appearance of objects lit by this illumination. We use this network to label a large-scale dataset of LDR panoramas with lighting parameters and use them to train our single image outdoor lighting estimation network. We demonstrate, via extensive experiments, that both our panorama and single image networks outperform the state of the art, and unlike prior work, are able to handle weather conditions ranging from fully sunny to overcast skies.