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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在超现实场景上进行深度内在分解训练，并具有逼真的灯光效果。

由于地面实况数据集的弱点（太小或存在非现实问题），估计固有图像仍然是一项艰巨的任务。另一方面，端到端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，则可以改善这些结果。在这项工作中，我们提出了一个双重框架：（a）灵活地生成图像，以克服一些经典的数据集问题，例如更大的尺寸以及连贯的照明外观；（b）通过内在损失将物理性质联系起来的灵活架构。我们的建议是通用的，计算时间短，并能达到最新的结果。