Adversarial Training With Channel Attention Regularization

The goal of intrinsic image decomposition is to recover low level features of images. Most of the studies tend to consider only reflectance and shading, even though it is known that increasing the number of intrinsics is beneficial for many applications. Existing intrinsic image datasets are quite limited. in this study, a dataset is introduced to provide a comprehensive benchmark to the field of intrinsic image decomposition. IID-NORD contains a large number of scenes and for each scene ground truth reflectance, shading, surface normal vectors, light vectors and depth map is provided to allow detailed decomposition. Moreover, diverse illuminants, viewing angles, and dynamic shadows are used to prevent any bias. To the best of available knowledge, IID-NORD is the most comprehensive dataset in the field of intrinsic image decomposition. IID-NORD will be available on the first author's official webpage.