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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SFB-TRR 161 INF "Collaboration Infrastructure"(Universität Stuttgart, Universität Konstanz)
SFB-TRR 161 INF "Collaboration Infrastructure" logo
May 14, 2020
Project INF supports the other projects of SFB/Transregio 161 by providing a central approach to data management and an infrastructure for virtual meetings in a large, high-resolution display scenario.
SFB-TRR 161 D03 "Visual Exploration and Analysis of Provenance Data" logo
Aug 4, 2023
To analyse or debug complex data processing applications, or to ensure their understandability and repeatability, provenance techniques are increasingly being deployed, resulting in large volumes and a wide variety of provenance data. The long-term goal of this project is to leve...
SFB-TRR 161 D02 "Evaluation Metrics for Visual Analytics in Linguistics" logo
Aug 4, 2023
Within linguistics, the use of large sets of data via a combination of rule-based and stochastic methods is now standardly part of the analysis of language structure. However, though scatter plots, bar or pie charts, and trees as provided by R, for example, are standardly used, n...
SFB-TRR 161 D01 "Perception-Guided Adaptive Modeling of 3D Virtual Cities Based on Probabilistic Grammars" logo
Aug 4, 2023
The goal of the project is to create quantitative methods for the perception-aware representation of 3D virtual cities. We will develop a grammar-based system for the effective visual communication of building-related information via geometric 3D building representations that ind...
SFB-TRR 161 C07 "Optimization for Dynamic Mixed Reality User Interfaces" logo
Aug 4, 2023
In this project we aim to dynamically adapt the user interface during interaction with Cross-Reality (XR) applications through head-mounted displays (HMDs), to improve usability and ensure the users safety and comfort.
SFB-TRR 161 C06 "User-Adaptive Mixed Reality"(Ludwig-Maximilians-Universität München)
SFB-TRR 161 C06 "User-Adaptive Mixed Reality" logo
Dec 6, 2021
SFB-TRR 161 C05 "Human-Machine Interaction with Adaptive Multisensory Systems" logo
Aug 4, 2023
In this project, we will carry out behavioural experiments using human participants and base our empirical choices on the framework of optimal decision theory as derived from the Bayesian approach. This approach can be used as a tool to construct ideal observer models against whi...
SFB-TRR 161 C04 "Metrics for Mobile Visualization and Interaction Techniques through 'Research in the Large'" logo
Aug 4, 2023
Models and metrics of visualisation and interaction techniques can predict the time a person needs to understand presented information and to execute a task. Previous work mainly focused on basic models. What is missing are models and metrics for realistic use cases and novel int...
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