Space-time volumetric depth images for in-situ visualization

Fernandes, Oliver; Frey, Steffen; Sadlo, Filip; Ertl, Thomas

doi:10.1109/ldav.2014.7013205

Cited by 21 publications

(8 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the specified values for the three groups of parameters, we generate the corresponding visualization images. Our work uses RGB images compressed to the portable network graphics (PNG) format instead of more sophisticated image formats, such as volumetric depth images [21,22] or explorable images [59][60][61], for two reasons. First, the benefits of using those sophisticated image formats, such as supporting changing of viewpoints, can be achieved by InSituNet trained on the RGB images.…”

Section: In Situ Training Data Collectionmentioning

confidence: 99%

“…Frey et al [22] proposed volumetric depth images, a compact representation of volumetric data that can be rendered efficiently with arbitrary viewpoints. Fernandes et al [21] later extended volumetric depth images to handle time-varying volumetric data. Biedert and Garth [8] combined topology analysis and imagebased data representation to preserve flexibility for post-hoc exploration and analysis.…”

Section: Image-based In Situ Visualizationmentioning

confidence: 99%

“…For example, the Cinema framework [1,2] visualized the simulation data from different viewpoints in situ and collected images to support post-hoc exploration. The volumetric depth images [21,22] stored ray segments with composited color and opacity values to enable post-hoc exploration of arbitrary viewpoints for volume rendering. However, these approaches focus more on extending the capability to explore the visual mapping parameters (e.g., transfer functions) and view parameters (e.g., view angles) and have little consideration of the simulation parameters, which are important in studying ensemble simulations.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

InSituNet: Deep Image Synthesis for Parameter Space Exploration of Ensemble Simulations

Wang

Guo

et al. 2019

IEEE Trans. Visual. Comput. Graphics

View full text Add to dashboard Cite

We propose InSituNet, a deep learning based surrogate model to support parameter space exploration for ensemble simulations that are visualized in situ. In situ visualization, generating visualizations at simulation time, is becoming prevalent in handling large-scale simulations because of the I/O and storage constraints. However, in situ visualization approaches limit the flexibility of post-hoc exploration because the raw simulation data are no longer available. Although multiple image-based approaches have been proposed to mitigate this limitation, those approaches lack the ability to explore the simulation parameters. Our approach allows flexible exploration of parameter space for large-scale ensemble simulations by taking advantage of the recent advances in deep learning. Specifically, we design InSituNet as a convolutional regression model to learn the mapping from the simulation and visualization parameters to the visualization results. With the trained model, users can generate new images for different simulation parameters under various visualization settings, which enables in-depth analysis of the underlying ensemble simulations. We demonstrate the effectiveness of InSituNet in combustion, cosmology, and ocean simulations through quantitative and qualitative evaluations.Index Terms-In situ visualization, ensemble visualization, parameter space exploration, deep learning, image synthesis.

show abstract

Section: In Situ Training Data Collectionmentioning

confidence: 99%

Section: Image-based In Situ Visualizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

InSituNet: Deep Image Synthesis for Parameter Space Exploration of Ensemble Simulations

Wang

Guo

et al. 2019

IEEE Trans. Visual. Comput. Graphics

View full text Add to dashboard Cite

show abstract

“…Lists for all rays form a VDI, which can be used to create images that are equivalent to their corresponding volume renderings. This technique was also been extended to deal with time series of volumes, as reported in [FFSE14]. Data reduction achieved through VDIs has been reported to be approximately one order of magnitude.…”

Section: Image Space Visualizationmentioning

confidence: 99%

Data Reduction Techniques for Simulation, Visualization and Data Analysis

Marsaglia

Garth

et al. 2018

Computer Graphics Forum

View full text Add to dashboard Cite

Data reduction is increasingly being applied to scientific data for numerical simulations, scientific visualizations and data analyses. It is most often used to lower I/O and storage costs, and sometimes to lower in‐memory data size as well. With this paper, we consider five categories of data reduction techniques based on their information loss: (1) truly lossless, (2) near lossless, (3) lossy, (4) mesh reduction and (5) derived representations. We then survey available techniques in each of these categories, summarize their properties from a practical point of view and discuss relative merits within a category. We believe, in total, this work will enable simulation scientists and visualization/data analysis scientists to decide which data reduction techniques will be most helpful for their needs.

show abstract

“…Nouanesengsy et al, 2014 [NWP*14] introduced a scheme around Analysis‐Driven Refinement (ADR), also inspired by the principles behind adaptive mesh refinement. Fernandes et al, 2014 [FFSE14] extend volumetric depth images (VDI) for compression of simulation results for post hoc visualization. Their method takes advantage of space‐time coherence of time‐dependent simulation data to obtain in situ data reduction.…”

Section: In Situ History and Surveymentioning

confidence: 99%

In Situ Methods, Infrastructures, and Applications on High Performance Computing Platforms

Bauer

Abbasi

Ahrens

et al. 2016

Computer Graphics Forum

144

View full text Add to dashboard Cite

The considerable interest in the high performance computing (HPC) community regarding analyzing and visualization data without first writing to disk, i.e., in situ processing, is due to several factors. First is an I/O cost savings, where data is analyzed/visualized while being generated, without first storing to a filesystem. Second is the potential for increased accuracy, where fine temporal sampling of transient analysis might expose some complex behavior missed in coarse temporal sampling. Third is the ability to use all available resources, CPU's and accelerators, in the computation of analysis products. This STAR paper brings together researchers, developers and practitioners using in situ methods in extreme-scale HPC with the goal to present existing methods, infrastructures, and a range of computational science and engineering applications using in situ analysis and visualization.

show abstract

Space-time volumetric depth images for in-situ visualization

Cited by 21 publications

References 18 publications

InSituNet: Deep Image Synthesis for Parameter Space Exploration of Ensemble Simulations

InSituNet: Deep Image Synthesis for Parameter Space Exploration of Ensemble Simulations

Data Reduction Techniques for Simulation, Visualization and Data Analysis

In Situ Methods, Infrastructures, and Applications on High Performance Computing Platforms

Contact Info

Product

Resources

About