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 3,634 Results
ZIP Archive - 5.3 MB - MD5: 3921141fef225b964e15feea89c53185
Contains for each perspective chosen by a user the 2D projection as a GraphML file including the projected 2D node positions.
ZIP Archive - 2.6 GB - MD5: 6c0dc1fa0ca6cbd5dc9da20ca1dbb091
The 2D projections of all selected perspectives as image (PNG).
ZIP Archive - 2.1 MB - MD5: 244dee84ea1532aaecca3dff79301c6c
The distribution of selected perspectives (best / worst) visualised as a spherical representation.
ZIP Archive - 604.1 MB - MD5: 360bca26060089914cf383905fe3eaa9
Contains for each graph the 2D projections (GraphML) and 3D positions for 5000 sampled perspectives (Fibonacci Lattice). These were used to find aesthetics minima and maxima for normalisation.
ZIP Archive - 17.0 MB - MD5: 9ecc831d0ac8d2312104422ddde78cfc
The calculation results of the 2D aesthetics measures (implemented in GdMetriX) without normalisation (chosen and sampled perspectives).
ZIP Archive - 30.3 KB - MD5: 760e4146671621135c8228850c11b941
The calculation results of the 3D overlap measures without normalisation for the perspectives chosen by users.
ZIP Archive - 5.3 MB - MD5: 8935bb6157ac244650a085ad02de7d4b
The calculation results of the 3D overlap measures without normalisation for the 5000 sampled perspectives.
ZIP Archive - 18.6 KB - MD5: bee8cc185b247659cc33c79727c36361
The calculation results of the 3D ISO measure without normalisation for the perspectives chosen by users.
ZIP Archive - 2.0 MB - MD5: c809bae6b72e974499b4c35701d0d7c9
The calculation results of the 3D ISO measure without normalisation for the 5000 sampled perspectives.
ZIP Archive - 259.7 KB - MD5: d6241aa76c48d0db72fd965970b3a814
The normalised aesthetic measure results for all user-selected perspectives, all graphs, and all measures. Normalisation is based on the minimum and maximum aesthetic measure values as found by the 5000 sampled viewpoints.
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