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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PNG Image - 42.3 KB - MD5: d7fdd93410b4182a35946b95b5945ef7
The figure referenced in the paper, showing the correlations between the two methods for each gross brain region.
Adobe PDF - 73.3 KB - MD5: bb0bad69b8b4a0702c8e6349da95d289
The exact correlation values, shown in the accompanying figure
May 14, 2025 - SFB-TRR 161 B04 "Adaptive Algorithms for Motion Estimation"
Schmalfuss, Jenny; Oei, Victor; Mehl, Lukas; Bartsch, Madlen; Agnihotri, Shashank; Keuper, Margret; Bruhn, Andres, 2025, "RobustSpring: Benchmarking Robustness to Image Corruptions for Optical Flow, Scene Flow and Stereo", https://doi.org/10.18419/DARUS-5047, DaRUS, V1
The RobustSpring dataset contains the image corruption data files for scene flow, optical flow and stereo estimation with the Spring dataset. Note that this repository contains only the Spring test data files. For easier handling, we organized them into sub-directories by image corruption type: brightness.zip : brightness image corruption contrast....
ZIP Archive - 6.1 GB - MD5: fd328272ee8258f40ff60cd1d2788495
ZIP Archive - 2.8 GB - MD5: 9df74aff27e78a82e95aa69b06f450a0
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ZIP Archive - 2.4 GB - MD5: 55f0fbe8cc735bade8f79f1f16bf6e37
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