A Survey on Intrinsic Images: Delving Deep Into Lambert and Beyond

Elena Garces, Carlos Rodriguez-Pardo, Dan Casas, Jorge Lopez-Moreno

Abstract:

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 physicallyprincipled 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.