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Intrinsic image decomposition (IID) is an under-constrained problem.Therefore, traditional approaches use hand crafted priors to constrain theproblem. However, these constraints are limited when coping with complexscenes. Deep learning-based approaches learn these constraints implicitlythrough the data, but they often suffer from dataset biases (due to not beingable to include all possible imaging conditions).  In this paper, a combination of the two is proposed. Component specificpriors like semantics and invariant features are exploited to obtainsemantically and physically plausible reflectance transitions. Thesetransitions are used to steer a progressive CNN with implicit homogeneityconstraints to decompose reflectance and shading maps.  An ablation study is conducted showing that the use of the proposed priorsand progressive CNN increase the IID performance. State of the art performanceon both our proposed dataset and the standard real-world IIW dataset shows theeffectiveness of the proposed method. Code is made available athttps://github.com/Morpheus3000/SIGNet

SIGNet: Intrinsic Image Decomposition by a Semantic and Invariant Gradient Driven Network for Indoor Scenes