  In general, intrinsic image decomposition algorithms interpret shading as one
unified component including all photometric effects. As shading transitions are
generally smoother than reflectance (albedo) changes, these methods may fail in
distinguishing strong photometric effects from reflectance variations.
Therefore, in this paper, we propose to decompose the shading component into
direct (illumination) and indirect shading (ambient light and shadows)
subcomponents. The aim is to distinguish strong photometric effects from
reflectance variations. An end-to-end deep convolutional neural network
(ShadingNet) is proposed that operates in a fine-to-coarse manner with a
specialized fusion and refinement unit exploiting the fine-grained shading
model. It is designed to learn specific reflectance cues separated from
specific photometric effects to analyze the disentanglement capability. A
large-scale dataset of scene-level synthetic images of outdoor natural
environments is provided with fine-grained intrinsic image ground-truths. Large
scale experiments show that our approach using fine-grained shading
decompositions outperforms state-of-the-art algorithms utilizing unified
shading on NED, MPI Sintel, GTA V, IIW, MIT Intrinsic Images, 3DRMS and SRD
datasets.
