  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.
