Locus filters.

In this paper, directly following from Gage [J. Opt. Soc.

Deep spectral reflectance and illuminant estimation from self-interreflections.

In this work, we propose a convolutional neural network based approach to estimate the spectral reflectance of a surface and spectral power distribution of 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.

Spectral reflectance estimation from one RGB image using self-interreflections in a concave object.

Light interreflections occurring in a concave object generate a color gradient that is characteristic of the object's spectral reflectance. In this paper, we use this property in order to estimate the spectral reflectance of matte, uniformly colored, V-shaped surfaces from a single RGB image taken under directional lighting. First, simulations show that using one image of the concave object is equivalent to, and can even outperform, the state-of-the-art approaches based on three images taken under three lightings with different colors.