论文标题
一种使用合成数据集深入学习估算反射图和材料的方法
A Method for Estimating Reflectance map and Material using Deep Learning with Synthetic Dataset
论文作者
论文摘要
由于问题的固有性质固有的性质，将目标图像分解为内部属性的过程是一项艰巨的任务。训练网络所需的数据缺乏是很难进行分解外观任务的原因之一。在本文中，我们提出了一个基于深度学习的反射图预测系统，以估算图像中目标对象的材料估计，以减轻此图像分解操作中发生的不良问题。我们还提出了一个用于双向反射分布函数（BRDF）参数估计的网络体系结构，环境图估计。我们还使用合成数据来解决缺乏数据问题。我们摆脱了以前建议的基于深度学习的网络体系结构，用于反射率图，我们新建议使用条件生成对抗网络（CGAN）结构来估计反射率图，这可以在许多应用程序中更好地产生结果。为了提高该结构学习的效率，我们使用目标对象的正常地图新近利用了损失函数。
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.
