  Intrinsic image decomposition, which is an essential task in computer vision,
aims to infer the reflectance and shading of the scene. It is challenging since
it needs to separate one image into two components. To tackle this,
conventional methods introduce various priors to constrain the solution, yet
with limited performance. Meanwhile, the problem is typically solved by
supervised learning methods, which is actually not an ideal solution since
obtaining ground truth reflectance and shading for massive general natural
scenes is challenging and even impossible. In this paper, we propose a novel
unsupervised intrinsic image decomposition framework, which relies on neither
labeled training data nor hand-crafted priors. Instead, it directly learns the
latent feature of reflectance and shading from unsupervised and uncorrelated
data. To enable this, we explore the independence between reflectance and
shading, the domain invariant content constraint and the physical constraint.
Extensive experiments on both synthetic and real image datasets demonstrate
consistently superior performance of the proposed method.
