The Transregional Collaborative Research Centre 161 “Quantitative Methods for Visual Computing” is an interdisciplinary research centre at the University of Stuttgart and the University of Konstanz, funded by Deutsche Forschungsgemeinschaft (DFG) under project number 251654672. Ulm University and Ludwig-Maximilians-Universität München are participating institutions in the second and third funding period. The Max Planck Institute for Biological Cybernetics in Tübingen in was a participating institution in the first funding period.

The goal of SFB/Transregio 161 is establishing the paradigm of quantitative science in the field of visual computing, which is a long-term endeavour requiring a fundamental research effort broadly covering four research areas, namely quantitative models and measures, adaptive algorithms, interaction and applications. In the third funding period, which started in 2023, new research directions are being approached. One is visual explainability, assessing and quantifying how well the users of a visualisation system understand the phenomena shown visually. The second direction targets mixed reality, covering all forms of augmented and virtual reality as a cross-cutting field of various visual computing subfields, irrespective of applied technology. The third research theme aims to bring research results in the world, moving away from experiments in the laboratory and in the wild to openly accessible applications that provide research results, methods, data sets, and other outcomes from SFB/Transregio 161 to a wide range of stakeholders in academia, industry, teaching, and society in general.

In SFB/Transregio 161, approximately 40 scientists in the fields of computer science, visualisation, computer vision, human computer interaction, linguistics and applied psychology are jointly working on improving the quality of future visual computing methods and applications.

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31 to 40 of 69 Results
Dec 7, 2022 - SFB-TRR 161 A01 "Uncertainty Quantification and Analysis in Visual Computing"
Görtler, Jochen; Spinner, Thilo; Weiskopf, Daniel; Deussen, Oliver, 2022, "Replication Data for: Uncertainty-Aware Principal Component Analysis",, DaRUS, V1
This dataset contains the source code for uncertainty-aware principal component analysis (UA-PCA) and a series of images that show dimensionality reduction plots created with UA-PCA. The software is a JavaScript library for performing principal component analysis and dimensionali...
Nov 21, 2022 - SFB-TRR 161 D04 "Quantitative Aspects of Immersive Analytics for the Life Sciences"
Klein, Karsten; Garkov, Dimitar; Rütschlin, Sina; Böttcher, Thomas; Schreiber, Falk, 2022, "QSDB - a graphical Quorum Sensing Database: VANTED add-on source code",, DaRUS, V1
The add-on had been designed for the VANTED framework and used to create QSDB Database's collection of clickable networks. Each network is laid out according to SBGN standards, showing quorum sensing and quorum quenching interactions between organisms and signaling molecules. Thi...
Nov 9, 2022 - Visualisierungsinstitut der Universität Stuttgart
Krake, Tim; Klötzl, Daniel; Eberhardt, Bernhard; Weiskopf, Daniel, 2022, "Constrained Dynamic Mode Decomposition - Supplemental Material",, DaRUS, V1
In this supplemental material, we provide additional application results of constrained Dynamic Mode Decomposition and a parameter study for the delay parameter d that complement the evaluation in the main document: The paper on Constrained Dynamic Mode Decomposition published wi...
Oct 13, 2022 - SFB-TRR 161 A01 "Uncertainty Quantification and Analysis in Visual Computing"
Hägele, David; Krake, Tim; Weiskopf, Daniel, 2022, "Supplemental Material for Uncertainty-Aware Multidimensional Scaling",, DaRUS, V1
This dataset contains the supplemental material for "Uncertainty-Aware Multidimensional Scaling". Uncertainty-aware multidimensional scaling (UAMDS) is a nonlinear dimensionality reduction technique for sets of random vectors. This dataset consists of a PDF document that contains...
Oct 12, 2022 - Institute for Structural Mechanics
Krake, Tim; von Scheven, Malte, 2022, "Matlab Implementation of Efficient Updates of Redundancy Matrices",, DaRUS, V1
This is a Demo for the manuscript 'Efficient Update of Redundancy Matrices for Truss and Frame Structures' that demonstrates the speedup and accuracy of the proposed update formulas. The computation is done in single-precision. Please open and run the main file. Further informati...
Oct 11, 2022 - SFB-TRR 161 B07 "Computational Uncertainty Quantification"
Beschle, Cedric; Barth, Andrea, 2022, "Uncertainty visualization: Fundamentals and recent developments, code to produce data and visuals used in Section 5",, DaRUS, V1
Python code to generate the meshes and FEM solutions to Section 5 of the paper Uncertainty visualization: Fundamentals and recent developments. Comments are in the code to explain it. Paraview is used for the visualization.
Oct 5, 2022 - SFB-TRR 161 C06 "User-Adaptive Mixed Reality"
Dietz, Dennis; Oechsner, Carl; Ou, Changkun; Chiossi, Francesco; Sarto, Fabio; Mayer, Sven; Butz, Andreas, 2022, "Dataset and Analysis for "Walk This Beam: Impact of Different VR Balance Training Strategies and Height Exposure on Performance and Physiological Arousal"",, DaRUS, V1
Dynamic balance is an essential skill for the human upright gait; therefore, regular balance training can improve postural control and reduce the risk of injury. Even slight variations in walking conditions like height or ground conditions can significantly impact walking perform...
Sep 29, 2022 - SFB-TRR 161 INF "Collaboration Infrastructure"
Garkov, Dimitar; Müller, Christoph; Braun, Matthias; Weiskopf, Daniel; Schreiber, Falk, 2022, ""Research Data Curation in Visualization : Position Paper" (Data)",, DaRUS, V1, UNF:6:yUhRXAoSoLD387EnHtthFg== [fileUNF]
Here, we make available the supplemental material regarding data collection from the publicaiton "Research Data Curation in Visualization : Position Paper". The dataset represents an aggregated collection of the data policies of selected publication venues in the areas of visuali...
Sep 23, 2022 - SFB-TRR 161 A05 "Image/Video Quality Assessment: From Test Databases to Similarity-Aware and Perceptual Dynamic Metrics"
Hosu, Vlad; Lin, Hanhe; Szirányi, Tamas; Saupe, Dietmar, 2022, "KonIQ-10k IQA Database",, DaRUS, V1, UNF:6:2Pw1H0RwWd4G4kLDsT/bjA== [fileUNF]
KonIQ-10k is, at the time of publication, the largest IQA dataset to date consisting of 10,073 quality scored images. This is the first in-the-wild database aiming for ecological validity, with regard to the authenticity of distortions, the diversity of content, and quality-relat...
Sep 21, 2022 - SFB-TRR 161 B01 "Adaptive Self-Consistent Visualization"
Rodrigues, Nils; Schulz, Christoph; Döring, Sören; Baumgartner, Daniel; Krake, Tim; Weiskopf, Daniel, 2022, "Supplemental Material for Relaxed Dot Plots: Faithful Visualization of Samples and Their Distribution",, DaRUS, V1
Supplemental material for the paper "Relaxed Dot Plots: Faithful Visualization of Samples and Their Distribution". Contains: math behind Relaxed Dot Plots additional images pseudo-anonymous study data source code for library and test application To view the material, extract supp...
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