# DeLight-Net: Decomposing Reflectance Maps into Specular Materials and Natural Illumination

arXiv: https://arxiv.org/abs/1603.08240 · HTML: https://mlsanity.com/paper/1603.08240/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Mar 2016.

## Built on by (most cited first, 3 of 3)

- [Shape and Material Capture at Home](https://mlsanity.com/paper/2104.06397/) (2021)
- [Diffusion Reflectance Map: Single-Image Stochastic Inverse Rendering of Illumination and Reflectance](https://mlsanity.com/paper/2312.04529/) (2023)
- [Under One Sun: Multi-Object Generative Perception of Materials and Illumination](https://mlsanity.com/paper/2603.19226/) (2026)

## Abstract (arXiv)

In this paper we are extracting surface reflectance and natural environmental illumination from a reflectance map, i.e. from a single 2D image of a sphere of one material under one illumination. This is a notoriously difficult problem, yet key to various re-rendering applications. With the recent advances in estimating reflectance maps from 2D images their further decomposition has become increasingly relevant. To this end, we propose a Convolutional Neural Network (CNN) architecture to reconstruct both material parameters (i.e. Phong) as well as illumination (i.e. high-resolution spherical illumination maps), that is solely trained on synthetic data. We demonstrate that decomposition of synthetic as well as real photographs of reflectance maps, both in High Dynamic Range (HDR), and, for the first time, on Low Dynamic Range (LDR) as well. Results are compared to previous approaches quantitatively as well as qualitatively in terms of re-renderings where illumination, material, view or shape are changed.