Neural networks have shown great success in extracting geometric information from color images. Especially, monocular depth estimation networks are increasingly reliable in real-world scenes. In this work we investigate the applicability of such monocular depth estimation networks to semi-transparent volume rendered images. As depth is notoriously difficult to define in a volumetric scene without clearly defined surfaces, we consider different depth computations that have emerged in practice, and compare state-of-the-art monocular depth estimation approaches for these different interpretations during an evaluation considering different degrees of opacity in the renderings. Additionally, we investigate how these networks can be extended to further obtain color and opacity information, in order to create a layered representation of the scene based on a single color image. This layered representation consists of spatially separated semi-transparent intervals that composite to the original input rendering. In our experiments we show that adaptions of existing approaches to monocular depth estimation perform well on semi-transparent volume renderings, which has several applications in the area of scientific visualization.

翻译：神经网络在从彩色图像中提取几何信息方面表现出极大的成功。 特别是, 单层深度估计网络在现实世界的场景中越来越可靠。 在这项工作中, 我们调查了这种单层深度估计网络对半透明体积图像的适用性。 由于深度在体积场景中很难定义而没有明确界定表面, 我们考虑实际中出现的不同深度计算, 并在一项评估中比较这些不同解释的最新水平单层深度估计方法。 此外, 我们调查如何扩大这些网络以进一步获取颜色和不透明性信息, 以便在单一颜色图像的基础上建立场景的层表层代表。 这种层表层代表由空间上分离的半透明间隔组成, 与最初输入的图像相融合。 在我们的实验中, 我们发现, 现有单层深度估计方法的调整在半透明体积显示效果良好, 半透明体积在科学可视化领域有若干用途 。