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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21 to 30 of 69 Results
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 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 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...
Aug 1, 2023 - SFB-TRR 161 D04 "Quantitative Aspects of Immersive Analytics for the Life Sciences"
Feyer, Stefan Paul; Pinaud, Bruno; Kobourov, Stephen; Brich, Nicolas; Krone, Michael; Kerren, Andreas; Schreiber, Falk; Klein, Karsten, 2023, "Supplemental Material: "2D, 2.5D, or 3D? An Exploratory Study on Multilayer Network Visualizations in Virtual Reality"", https://doi.org/10.18419/darus-3387, DaRUS, V1, UNF:6:PSjD1vZ/FkCWAinPKFoFOg== [fileUNF]
Dataset containing supplemental material for the publication "2D, 2.5D, or 3D? An Exploratory Study on Multilayer Network Visualizations in Virtual Reality" This dataset contains: 1) archive containing all raw quantitative results, 2) archive containing all raw qualitative data,...
Jul 17, 2023 - SFB-TRR 161 B04 "Adaptive Algorithms for Motion Estimation"
Mehl, Lukas; Schmalfuss, Jenny; Jahedi, Azin; Nalivayko, Yaroslava; Bruhn, Andrés, 2023, "Spring: A High-Resolution High-Detail Dataset and Benchmark for Scene Flow, Optical Flow and Stereo", https://doi.org/10.18419/darus-3376, DaRUS, V2
The Spring dataset contains files for scene flow, optical flow and stereo estimation. For easier handling, we organized them into sub-directories: train split: train_frame_left.zip: left camera frames train_frame_right.zip: right camera frames train_disp1_left.zip: left-to-right...
Jun 26, 2023 - SFB-TRR 161 A07 "Visual Attention Modeling for Optimization of Information Visualizations"
Wang, Yao, 2023, "Data for: "Scanpath Prediction on Information Visualizations"", https://doi.org/10.18419/darus-3361, DaRUS, V2, UNF:6:cqkNueYjBVCLYaXEqJq3yw== [fileUNF]
We propose Unified Model of Saliency and Scanpaths (UMSS) - a model that learns to predict multi-duration saliency and scanpaths (i.e. sequences of eye fixations) on information visualisations. Although scanpaths provide rich information about the importance of different visualis...
Jun 16, 2023 - Institute for Structural Mechanics
Tkachuk, Anton; Krake, Tim; Gade, Jan; von Scheven, Malte, 2023, "Matlab Implementation of Efficient Computation of Redundancy Matrices", https://doi.org/10.18419/darus-3347, DaRUS, V1
This is a Demo for the manuscript 'Efficient Computation of Redundancy Matrices for Moderately Redundant Truss and Frames Structures' that demonstrates the speedup of the proposed algorithms. The computation is done in single-precision. Please open and run the main file. Further...
Mar 6, 2023 - SFB-TRR 161 A03 "Quantification of Visual Analytics Transformations and Mappings"
Pomerenke, David; Dennig, Frederik L.; Keim, Daniel; Fuchs, Johannes; Blumenschein, Michael, 2022, "Replication Data for: "Slope-Dependent Rendering of Parallel Coordinates to Reduce Density Distortion and Ghost Clusters"", https://doi.org/10.18419/darus-3060, DaRUS, V2, UNF:6:UBKuKSiQ9Yl4rH7r00rY3g== [fileUNF]
This is the replication data for our publication "Slope-Dependent Rendering of Parallel Coordinates to Reduce Density Distortion and Ghost Clusters." It contains the datasets and the code used to render optimized Parallel Coordinate Plots. We used the following 36 datasets for ou...
Feb 9, 2023 - SFB-TRR 161 C01 "Quantifying Interaction"
Hubenschmid, Sebastian; Zagermann, Johannes; Leicht, Daniel; Reiterer, Harald; Feuchtner, Tiare, 2023, "ARound the Smartphone: Investigating the Effects of Virtually-Extended Display Size on Spatial Memory", https://doi.org/10.18419/darus-3326, DaRUS, V1, UNF:6:DoWRbv6yFS/mgJ7b89Af6Q== [fileUNF]
Data set for the gathered user study data of the paper "ARound the Smartphone: Investigating the Effects of Virtually-Extended Display Size on Spatial Memory" (CHI'23). Paper Abstract: Smartphones conveniently place large information spaces in the palms of our hands. While resear...
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