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We propose a novel intrinsic image decomposition network consideringreflectance consistency. Intrinsic image decomposition aims to decompose animage into illumination-invariant and illumination-variant components, referredto as ``reflectance'' and ``shading,'' respectively. Although there are threeconsistencies that the reflectance and shading should satisfy, mostconventional work does not sufficiently account for consistency with respect toreflectance, owing to the use of a white-illuminant decomposition model and thelack of training images capturing the same objects under variousillumination-brightness and -color conditions. For this reason, the threeconsistencies are considered in the proposed network by using acolor-illuminant model and training the network with losses calculated fromimages taken under various illumination conditions. In addition, the proposednetwork can be trained in a self-supervised manner because various illuminationconditions can easily be simulated. Experimental results show that our networkcan decompose images into reflectance and shading components.

Self-Supervised Intrinsic Image Decomposition Network Considering Reflectance Consistency