  The process of decomposing target images into their internal properties is a
difficult task due to the inherent ill-posed nature of the problem. The lack of
data required to train a network is a one of the reasons why the decomposing
appearance task is difficult. In this paper, we propose a deep learning-based
reflectance map prediction system for material estimation of target objects in
the image, so as to alleviate the ill-posed problem that occurs in this image
decomposition operation. We also propose a network architecture for
Bidirectional Reflectance Distribution Function (BRDF) parameter estimation,
environment map estimation. We also use synthetic data to solve the lack of
data problems. We get out of the previously proposed Deep Learning-based
network architecture for reflectance map, and we newly propose to use
conditional Generative Adversarial Network (cGAN) structures for estimating the
reflectance map, which enables better results in many applications. To improve
the efficiency of learning in this structure, we newly utilized the loss
function using the normal map of the target object.
