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.

中文翻译：

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在超现实场景中训练的深度内在分解但具有逼真的灯光效果

由于真实数据集的弱点，内在图像的估计仍然是一项具有挑战性的任务，这些数据集要么太小，要么存在不切实际的问题。另一方面，端到端的深度学习架构开始取得有趣的结果，我们认为如果不忽略重要的物理提示，这些结果可以得到改进。在这项工作中，我们提出了一个双重框架：(a) 灵活的图像生成，克服了一些经典的数据集问题，例如较大的尺寸和相干的照明外观；(b) 一种灵活的架构，通过内在损失将物理特性联系起来。我们的提议是通用的，计算时间短，并取得了最先进的结果。