Intrinsic image decomposition using physics-based cues and CNNs

Intrinsic image decomposition is the decomposition of an image into its reflectance and shading components. The intrinsic image decomposition problem is inherently ill-posed, since there can be multiple solutions to compute the intrinsic components forming the same image. In this paper, we explore the use of physics-based priors. We also propose a new architecture that separates the learning components in a stacked manner. We explore various ways of integrating such priors into a deep learning system. Our method is trained and tested on a large synthetic garden dataset to assess its performance. It is evaluated and compared to state-of-the-art methods using two standard intrinsic datasets. Finally, the pre-trained network is tested on real world images to show the generalisation capabilities of the network.

Automated summary

This study explores the intrinsic image decomposition problem, introducing physics-based priors and a new architecture to address it, and evaluates and compares the method on synthetic and real-world datasets.