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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3,631 to 3,640 of 3,690 Results
Mar 8, 2022 - SFB-TRR 161 C06 "User-Adaptive Mixed Reality"
Huang, Ann; Knierim, Pascal; Chiossi, Francesco; Chuang, Lewis; Welsch, Robin, 2022, "Proxemics for Human-Agent Interaction in Augmented Reality", https://doi.org/10.18419/DARUS-2525, DaRUS, V1, UNF:6:gMC1ZC3kIcnTw0ymCcJbgQ== [fileUNF]
We report an experiment (N=54) where participants interacted with agents in an AR (Augmented Reality) art gallery scenario. When participants approached six virtual agents (i.e., two males, two females, a humanoid robot, and a pillar) to ask for directions, we found that participants respected the agents’ PS (Personal Space) and modulated interpers...
Tabular Data - 37.0 KB - 7 Variables, 540 Observations - UNF:6:zLLTaoJyI4/oSsqd1A78/g==
Dataframe with Electrodermal Activity (EDA) Data
Tabular Data - 129.5 KB - 13 Variables, 1068 Observations - UNF:6:1BTACawKEQZOICjP20Y7yw==
Dataframe with minimum/passing distances of each participant
Tabular Data - 112.3 KB - 13 Variables, 926 Observations - UNF:6:UXU37lC46OcL0ZjIEAzlRA==
Dataframe with each participant's preferred IPD, as well as passing distances with respect to each virtual agent (separated by sex) on each trial for both experimental blocks
R Syntax - 9.7 KB - MD5: 4fe9f0ea8b83f11c078b34baa0079992
Data analysis Code
Tabular Data - 2.7 KB - 3 Variables, 252 Observations - UNF:6:PP1EuYfA1WaWRDaJj4iRBA==
Dataframe with Likeability Data. Participants' responses for the likeability of the virtual agents recorded on a 5 point Likert-Scale: 1 = "I strongly dislike the agent"; 5 = "I strongly like the agent"
Tabular Data - 978 B - 4 Variables, 54 Observations - UNF:6:KI4Fyb6PjhJNuL7/Y10ARQ==
Dataframe with demographics data.
Tabular Data - 4.4 KB - 7 Variables, 51 Observations - UNF:6:0Pmp+AHdf7fH9Gdom/V4Dg==
Dataframe with perceived gender. Column names "F1", "F2", "M1", "M2", "robot", "Pillar" correspond to the name of the virtual agents used in the study. The order and their appearances are the same as Figure 2 in the manuscript. F1 stands for "Female 1"; F2 stands for "Female 2"; M1 stands for "Male 1", M2 stands for "Male 2". The folder contains t...
SFB-TRR 161 B05 "Efficient Large Scale Variational 3D Reconstruction" logo
Dec 16, 2021
The central goal of the project is to research and develop high-performance variational methods for large scale 3D reconstruction problems, which are general and accurate while meeting computation time constraints imposed by visual computing applications.
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