Reviews: Self-Supervised Intrinsic Image Decomposition

The paper presents an interesting approach on the intrinsic image decomposition problem: given an input rgb image, it decomposes it first into shape (normals), reflectance (albedo) and illumination (point light) using an encoder-decoder deep architecture with 3 outputs. Then there is another encoder-decoder that takes the predicted normals and light and outputs the shading of the shape. Finally, the result comes from a multiplication between the estimated reflectance (from the 1st encoder-decoder) with the estimated shading. The idea of having a reconstruction loss to recover the input image is interesting, but I believe that is only partially employed in the paper. The network architecture still needs labeled data for the initial training.