Single Image Intrinsic Decomposition with Discriminative Feature Encoding

Abstract

Intrinsic image decomposition is an important and long-standing computer vision problem. Given a single input image, recovering the physical scene properties is ill-posed. In this work, we take the advantage of deep learning, which is proven to be highly efficient in solving the challenging computer vision problems including intrinsic image decomposition. Our focus lies in the feature encoding phase to extract discriminative features for different intrinsic layers from a single input image. To achieve this goal, we explore the distinctive characteristics between different intrinsic components in the high dimensional feature embedding space. We propose a feature divergence loss to force their high-dimensional embedding feature vectors to be separated efficiently. The feature distributions are also constrained to fit the real ones. In addition, we provide an approach to remove the data inconsistency in the MPI Sintel dataset, making it more proper for intrinsic image decomposition. Experimental results indicate that the proposed network structure is able to outperform the state-of-the-art methods.