  We propose a novel intrinsic image decomposition network considering
reflectance consistency. Intrinsic image decomposition aims to decompose an
image into illumination-invariant and illumination-variant components, referred
to as ``reflectance'' and ``shading,'' respectively. Although there are three
consistencies that the reflectance and shading should satisfy, most
conventional work does not sufficiently account for consistency with respect to
reflectance, owing to the use of a white-illuminant decomposition model and the
lack of training images capturing the same objects under various
illumination-brightness and -color conditions. For this reason, the three
consistencies are considered in the proposed network by using a
color-illuminant model and training the network with losses calculated from
images taken under various illumination conditions. In addition, the proposed
network can be trained in a self-supervised manner because various illumination
conditions can easily be simulated. Experimental results show that our network
can decompose images into reflectance and shading components.
