  Intrinsic imaging or intrinsic image decomposition has traditionally been
described as the problem of decomposing an image into two layers: a
reflectance, the albedo invariant color of the material; and a shading,
produced by the interaction between light and geometry. Deep learning
techniques have been broadly applied in recent years to increase the accuracy
of those separations. In this survey, we overview those results in context of
well-known intrinsic image data sets and relevant metrics used in the
literature, discussing their suitability to predict a desirable intrinsic image
decomposition. Although the Lambertian assumption is still a foundational basis
for many methods, we show that there is increasing awareness on the potential
of more sophisticated physically-principled components of the image formation
process, that is, optically accurate material models and geometry, and more
complete inverse light transport estimations. We classify these methods in
terms of the type of decomposition, considering the priors and models used, as
well as the learning architecture and methodology driving the decomposition
process. We also provide insights about future directions for research, given
the recent advances in neural, inverse and differentiable rendering techniques.
