We present a new deep supervised learning method for intrinsic decomposition of a single image into its albedo and shading components. Our contributions are based on a new fully convolutional neural network that estimates absolute albedo and shading jointly. As opposed to classical intrinsic image decomposition work, it is fully data-driven, hence does not require any physical priors like shading smoothness or albedo sparsity, nor does it rely on geometric information such as depth. Compared to recent deep learning techniques, we simplify the architecture, making it easier to build and train. It relies on a single end-to-end deep sequence of residual blocks and a perceptually-motivated metric formed by two discriminator networks. We train and demonstrate our architecture on the publicly available MPI Sintel dataset and its intrinsic image decomposition augmentation. We additionally discuss and augment the set of quantitative metrics so as to account for the more challenging recovery of non scale-invariant quantities. Results show that our work outperforms the state of the art algorithms both on the qualitative and quantitative aspect, while training convergence time is reduced.
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