论文标题
在超现实场景中训练的深度内在分解，但具有逼真的光效果
Deep intrinsic decomposition trained on surreal scenes yet with realistic light effects
论文作者
论文摘要
由于地面数据集的弱点，固有图像的估计仍然是一项具有挑战性的任务，该数据集的弱点太小或目前的非现实问题。另一方面，端到端的深度学习体系结构开始取得有趣的结果，如果不忽略重要的物理提示，我们认为可以改善这些结果。在这项工作中，我们提出了一个双重框架：（a）一系列灵活的图像克服了一些经典的数据集问题，例如较大尺寸，共同结合相干照明外观； （b）通过内在损失将物理特性绑定的灵活结构。我们的提议用途广泛，呈现较低的计算时间，并实现最先进的结果。
Estimation of intrinsic images still remains a challenging task due to weaknesses of ground-truth datasets, which either are too small or present non-realistic issues. On the other hand, end-to-end deep learning architectures start to achieve interesting results that we believe could be improved if important physical hints were not ignored. In this work, we present a twofold framework: (a) a flexible generation of images overcoming some classical dataset problems such as larger size jointly with coherent lighting appearance; and (b) a flexible architecture tying physical properties through intrinsic losses. Our proposal is versatile, presents low computation time, and achieves state-of-the-art results.
