Monocular Depth Decomposition of Semi-Transparent Volume Renderings
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Abstract (arXiv)
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 existing approaches to monocular depth estimation can be adapted to perform well on semi-transparent volume renderings, which has several applications in the area of scientific visualization, like re-composition with additional objects and labels or additional shading.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 20 of its 57 references that are in the map. First posted on arXiv 27 Jun 2022.
Builds on
20 of 57 references.
References · 20
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- 2022DL4SciVis: A State-of-the-Art Survey on Deep Learning for Scientific Visualizationcited by 32
8 more
- 2018Development and Validation of Deep Learning Algorithms for Detection of Critical Findings in Head CT Scanscited by 24
- 2019Volumetric Isosurface Rendering with Deep Learning-Based Super-Resolutioncited by 18
- 2020Domain Decluttering: Simplifying Images to Mitigate Synthetic-Real Domain Shift and Improve Depth Estimationcited by 17
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- 2020SelfDeco: Self-Supervised Monocular Depth Completion in Challenging Indoor Environmentscited by 13
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- 2021Deep Direct Volume Rendering: Learning Visual Feature Mappings From Exemplary Imagescited by 4
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Citing papers per year: 2026: 1.
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