A Method for Estimating Reflectance map and Material using Deep Learning with Synthetic Dataset
This addresses the challenge of material estimation in computer vision, but it is incremental as it builds on existing deep learning methods with a new network architecture.
The paper tackles the ill-posed problem of decomposing images into reflectance maps and material properties by proposing a deep learning-based system using a conditional Generative Adversarial Network (cGAN) and synthetic data, achieving better results in applications.
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.