SFB/Transregio 161 "Quantitative Methods for Visual Computing"

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71 to 80 of 82 Results

SFB-TRR 161 A01 "Uncertainty Quantification and Analysis in Visual Computing"(Universität Stuttgart, Universität Konstanz) Dec 10, 2021
SFB-TRR 161 A08 "A Learning-Based Research Methodology for Visualization"(Universität Stuttgart) Dec 8, 2021
SFB-TRR 161 D05 "Visual computing for ERP-based brain activity"(Universität Stuttgart) Dec 8, 2021
SFB-TRR 161 A07 "Visual Attention Modeling for Optimization of Information Visualizations"(Universität Stuttgart) Dec 7, 2021
SFB-TRR 161 B01 "Adaptive Self-Consistent Visualization"(Universität Stuttgart) Dec 7, 2021
SFB-TRR 161 C06 "User-Adaptive Mixed Reality"(Ludwig-Maximilians-Universität München) Dec 6, 2021
SFB-TRR 161 C01 "Quantifying Interaction"(Universität Konstanz) Dec 6, 2021
SFB-TRR 161 D04 "Quantitative Aspects of Immersive Analytics for the Life Sciences"(Universität Konstanz) Dec 6, 2021
SFB/Transregio 161 Data Management Plan 2019-2023 Jul 8, 2020 - SFB-TRR 161 INF "Collaboration Infrastructure" Müller, Christoph, 2020, "SFB/Transregio 161 Data Management Plan 2019-2023", https://doi.org/10.18419/DARUS-632, DaRUS, V1 The participating universities in SFB/Transregio 161 acknowledge the general importance of research data management as a vital issue for all of their work and provide increasing central support for long-term accessibility and reusability of data, documentation of methods and tools and privacy protection. However, technical and organisational offeri...
Runtime performance measurements of interactive visualisation algorithms May 28, 2020 - SFB-TRR 161 A02 "Quantifying Visual Computing Systems" Bruder, Valentin; Müller, Christoph; Frey, Steffen; Ertl, Thomas, 2020, "Runtime performance measurements of interactive visualisation algorithms", https://doi.org/10.18419/DARUS-810, DaRUS, V1 Runtime performance measurements for GPU-based direct volume rendering and GPU-based raycasting of spherical particles on ten different discrete graphics processing units from AMD and NVIDIA. The data set at hand systematically evaluates typical factors influencing performance of interactive visualisations like view-port size, data set size, camera...

SFB-TRR 161 A01 "Uncertainty Quantification and Analysis in Visual Computing"(Universität Stuttgart, Universität Konstanz)

Dec 10, 2021

SFB-TRR 161 A08 "A Learning-Based Research Methodology for Visualization"(Universität Stuttgart)

Dec 8, 2021

SFB-TRR 161 D05 "Visual computing for ERP-based brain activity"(Universität Stuttgart)

Dec 8, 2021

SFB-TRR 161 A07 "Visual Attention Modeling for Optimization of Information Visualizations"(Universität Stuttgart)

Dec 7, 2021

SFB-TRR 161 B01 "Adaptive Self-Consistent Visualization"(Universität Stuttgart)

Dec 7, 2021

SFB-TRR 161 C06 "User-Adaptive Mixed Reality"(Ludwig-Maximilians-Universität München)

Dec 6, 2021

SFB-TRR 161 C01 "Quantifying Interaction"(Universität Konstanz)

Dec 6, 2021

SFB-TRR 161 D04 "Quantitative Aspects of Immersive Analytics for the Life Sciences"(Universität Konstanz)

Dec 6, 2021

SFB/Transregio 161 Data Management Plan 2019-2023

Jul 8, 2020 - SFB-TRR 161 INF "Collaboration Infrastructure"

Müller, Christoph, 2020, "SFB/Transregio 161 Data Management Plan 2019-2023", https://doi.org/10.18419/DARUS-632, DaRUS, V1

The participating universities in SFB/Transregio 161 acknowledge the general importance of research data management as a vital issue for all of their work and provide increasing central support for long-term accessibility and reusability of data, documentation of methods and tools and privacy protection. However, technical and organisational offeri...

Runtime performance measurements of interactive visualisation algorithms

May 28, 2020 - SFB-TRR 161 A02 "Quantifying Visual Computing Systems"

Bruder, Valentin; Müller, Christoph; Frey, Steffen; Ertl, Thomas, 2020, "Runtime performance measurements of interactive visualisation algorithms", https://doi.org/10.18419/DARUS-810, DaRUS, V1

Runtime performance measurements for GPU-based direct volume rendering and GPU-based raycasting of spherical particles on ten different discrete graphics processing units from AMD and NVIDIA. The data set at hand systematically evaluates typical factors influencing performance of interactive visualisations like view-port size, data set size, camera...

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