Intrinsic Image Decomposition via Ordinal Shading
Chris Careaga Yağız Aksoy
Intrinsic decomposition is a fundamental mid-level vision problem that plays
a crucial role in various inverse rendering and computational photography
pipelines. Generating highly accurate intrinsic decompositions is an inherently
under-constrained task that requires precisely estimating continuous-valued
shading and albedo. In this work, we achieve high-resolution intrinsic
decomposition by breaking the problem into two parts. First, we present a dense
ordinal shading formulation using a shift- and scale-invariant loss in order to
estimate ordinal shading cues without restricting the predictions to obey the
intrinsic model. We then combine low- and high-resolution ordinal estimations
using a second network to generate a shading estimate with both global
coherency and local details. We encourage the model to learn an accurate
decomposition by computing losses on the estimated shading as well as the
albedo implied by the intrinsic model. We develop a straightforward method for
generating dense pseudo ground truth using our model's predictions and
multi-illumination data, enabling generalization to in-the-wild imagery. We
present an exhaustive qualitative and quantitative analysis of our predicted
intrinsic components against state-of-the-art methods. Finally, we demonstrate
the real-world applicability of our estimations by performing otherwise
difficult editing tasks such as recoloring and relighting.