# Deep Lighting Environment Map Estimation from Spherical Panoramas

arXiv: https://arxiv.org/abs/2005.08000 · HTML: https://mlsanity.com/paper/2005.08000/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 May 2020.

## Built on by (most cited first, 9 of 9)

- [Deep Learning for HDR Imaging: State-of-the-Art and Future Trends](https://mlsanity.com/paper/2110.10394/) (2021)
- [Virtual Home Staging: Inverse Rendering and Editing an Indoor Panorama under Natural Illumination](https://mlsanity.com/paper/2311.12265/) (2023)
- [In-N-Out: Towards Good Initialization for Inpainting and Outpainting](https://mlsanity.com/paper/2106.13953/) (2021)
- [360U-Former: HDR Illumination Estimation with Panoramic Adapted Vision Transformers](https://mlsanity.com/paper/2410.13566/) (2024)
- [Shape and Reflectance Reconstruction in Uncontrolled Environments by Differentiable Rendering](https://mlsanity.com/paper/2110.12975/) (2021)
- [MixLight: Borrowing the Best of both Spherical Harmonics and Gaussian Models](https://mlsanity.com/paper/2404.12768/) (2024)
- [Color Recognition in Challenging Lighting Environments: CNN Approach](https://mlsanity.com/paper/2402.04762/) (2024)
- [Indoor Heat Estimation from a Single Visible-Light Panorama](https://mlsanity.com/paper/2502.06973/) (2025)
- [HDR Environment Map Estimation with Latent Diffusion Models](https://mlsanity.com/paper/2507.21261/) (2025)

## Abstract (arXiv)

Estimating a scene's lighting is a very important task when compositing synthetic content within real environments, with applications in mixed reality and post-production. In this work we present a data-driven model that estimates an HDR lighting environment map from a single LDR monocular spherical panorama. In addition to being a challenging and ill-posed problem, the lighting estimation task also suffers from a lack of facile illumination ground truth data, a fact that hinders the applicability of data-driven methods. We approach this problem differently, exploiting the availability of surface geometry to employ image-based relighting as a data generator and supervision mechanism. This relies on a global Lambertian assumption that helps us overcome issues related to pre-baked lighting. We relight our training data and complement the model's supervision with a photometric loss, enabled by a differentiable image-based relighting technique. Finally, since we predict spherical spectral coefficients, we show that by imposing a distribution prior on the predicted coefficients, we can greatly boost performance. Code and models available at https://vcl3d.github.io/DeepPanoramaLighting.