# Deep Spectral Reflectance and Illuminant Estimation from Self-Interreflections

## TL;DR

This paper introduces a CNN method that leverages interreflection effects in concave surfaces to accurately estimate spectral reflectance and illuminant spectra from a single RGB image, outperforming existing methods.

## Contribution

The work presents a novel deep learning approach trained on simulated physics-based data to jointly estimate spectral reflectance and illuminant spectra from single images.

## Key findings

- Outperforms state-of-the-art learning methods on simulated data
- Achieves better results on real data compared to other interreflection-based methods
- More robust to image noise than classical approaches

## Abstract

In this work, we propose a CNN-based approach to estimate the spectral reflectance of a surface and the spectral power distribution of the light from a single RGB image of a V-shaped surface. Interreflections happening in a concave surface lead to gradients of RGB values over its area. These gradients carry a lot of information concerning the physical properties of the surface and the illuminant. Our network is trained with only simulated data constructed using a physics-based interreflection model. Coupling interreflection effects with deep learning helps to retrieve the spectral reflectance under an unknown light and to estimate the spectral power distribution of this light as well. In addition, it is more robust to the presence of image noise than the classical approaches. Our results show that the proposed approach outperforms the state of the art learning-based approaches on simulated data. In addition, it gives better results on real data compared to other interreflection-based approaches.