In general, intrinsic image decomposition algorithms interpret shading as one unified component including all photometric effects. As shading transitions are generally smoother than reflectance (albedo) changes, these methods may fail in distinguishing strong photometric effects from reflectance variations. Therefore, in this paper, we propose to decompose the shading component into direct (illumination) and indirect shading (ambient light and shadows) subcomponents. The aim is to distinguish strong photometric effects from reflectance variations. An end-to-end deep convolutional neural network (ShadingNet) is proposed that operates in a fine-to-coarse manner with a specialized fusion and refinement unit exploiting the fine-grained shading model. It is designed to learn specific reflectance cues separated from specific photometric effects to analyze the disentanglement capability. A large-scale dataset of scene-level synthetic images of outdoor natural environments is provided with fine-grained intrinsic image ground-truths. Large scale experiments show that our approach using fine-grained shading decompositions outperforms state-of-the-art algorithms utilizing unified shading on NED, MPI Sintel, GTA V, IIW, MIT Intrinsic Images, 3DRMS and SRD datasets.

通常，固有图像分解算法将阴影解释为一个统一的组件，包括所有光度效应。由于阴影过渡通常比反射率（反照率）变化更平滑，因此这些方法可能无法将强光度学效果与反射率变化区分开。因此，在本文中，我们建议将阴影分量分解为直接（照明）子分量和间接阴影（环境光和阴影）子分量。目的是将强光度学效果与反射率变化区分开。提出了一种端到端的深度卷积神经网络（ShadingNet），该网络以细到粗的方式运行，并具有利用细化阴影模型的专用融合和细化单元。它旨在了解与特定光度效应分开的特定反射率线索，以分析解纠缠能力。提供了室外自然环境的场景级合成图像的大规模数据集，其中包含细粒度的固有图像地面真相。大规模实验表明，我们使用细粒度阴影分解的方法优于在NED，MPI Sintel，GTA V，IIW，MIT Intrinsic Images，3DRMS和SRD数据集上使用统一阴影的最新算法。