DARN: a Deep Adversial Residual Network for Intrinsic Image Decomposition

Understand

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.
• Our solution relies on a single end-to-end deep sequence of residual blocks and a perceptually-motivated metric formed by two adversarially trained discriminators.
• 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.