The 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 funding period, which started in 2019, following the Max Planck Institute for Biological Cybernetics in Tübingen 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 second funding period, new research directions are being approached, such as integrating machine learning, algorithms for real-time and dynamic adaptions and combining quantitative and qualitative methods. Furthermore SFB/Transregio 161 is looking into the quantification of immersion, acknowledging the raising interest in the whole spectrum of mixed reality applications in the recent past. Finally, the second funding period also addresses studies in the wild, working on evaluation methods for unconstrained studies.

In SFB/Transregio, 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.

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1 to 10 of 37 Results
Nov 21, 2022 - SFB-TRR 161 D04 "Quantitative Aspects of Immersive Analytics for the Life Sciences"
Klein, Karsten; Garkov, Dimitar; Rütschlin, Sina; Böttcher, Thomas; Schreiber, Falk, 2022, "QSDB - a graphical Quorum Sensing Database: VANTED add-on source code",, DaRUS, V1
The add-on had been designed for the VANTED framework and used to create QSDB Database's collection of clickable networks. Each network is laid out according to SBGN standards, showing quorum sensing and quorum quenching interactions between organisms and signaling molecules. Thi...
Oct 13, 2022 - SFB-TRR 161 A01 "Uncertainty Quantification and Analysis in Visual Computing"
Hägele, David; Krake, Tim; Weiskopf, Daniel, 2022, "Supplemental Material for Uncertainty-Aware Multidimensional Scaling",, DaRUS, V1
This dataset contains the supplemental material for "Uncertainty-Aware Multidimensional Scaling". Uncertainty-aware multidimensional scaling (UAMDS) is a nonlinear dimensionality reduction technique for sets of random vectors. This dataset consists of a PDF document that contains...
Oct 11, 2022 - SFB-TRR 161 B07 "Computational Uncertainty Quantification"
Beschle, Cedric, 2022, "Uncertainty visualization: Fundamentals and recent developments, code to produce data and visuals used in Section 5",, DaRUS, V1
Python Code to generate the meshes and FEM solutions to Section 5 of the paper Uncertainty visualization: Fundamentals and recent developments. Commentaries are in the code to explain it. Paraview is used for the visualization.
Oct 5, 2022 - SFB-TRR 161 C06 "User-Adaptive Mixed Reality"
Dietz, Dennis; Oechsner, Carl; Ou, Changkun; Chiossi, Francesco; Sarto, Fabio; Mayer, Sven; Butz, Andreas, 2022, "Dataset and Analysis for "Walk This Beam: Impact of Different VR Balance Training Strategies and Height Exposure on Performance and Physiological Arousal"",, DaRUS, V1
Dynamic balance is an essential skill for the human upright gait; therefore, regular balance training can improve postural control and reduce the risk of injury. Even slight variations in walking conditions like height or ground conditions can significantly impact walking perform...
Sep 29, 2022 - SFB-TRR 161 INF "Collaboration Infrastructure"
Garkov, Dimitar; Müller, Christoph; Braun, Matthias; Weiskopf, Daniel; Schreiber, Falk, 2022, ""Research Data Curation in Visualization : Position Paper" (Data)",, DaRUS, V1, UNF:6:yUhRXAoSoLD387EnHtthFg== [fileUNF]
Here, we make available the supplemental material regarding data collection from the publicaiton "Research Data Curation in Visualization : Position Paper". The dataset represents an aggregated collection of the data policies of selected publication venues in the areas of visuali...
Sep 27, 2022 - SFB-TRR 161 A03 "Quantification of Visual Analytics Transformations and Mappings"
Dennig, Frederik L., 2022, "Replication Data for: "Slope-Dependent Rendering of Parallel Coordinates to Reduce Density Distortion and Ghost Clusters"",, DaRUS, V1, 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 used to render optimized Parallel Coordinate Plots. We used the following 36 datasets for our experiments...
Sep 23, 2022 - SFB-TRR 161 A05 "Image/Video Quality Assessment: From Test Databases to Similarity-Aware and Perceptual Dynamic Metrics"
Hosu, Vlad; Lin, Hanhe; Szirányi, Tamas; Saupe, Dietmar, 2022, "KonIQ-10k IQA Database",, DaRUS, V1, UNF:6:2Pw1H0RwWd4G4kLDsT/bjA== [fileUNF]
KonIQ-10k is, at the time of publication, the largest IQA dataset to date consisting of 10,073 quality scored images. This is the first in-the-wild database aiming for ecological validity, with regard to the authenticity of distortions, the diversity of content, and quality-relat...
Sep 21, 2022 - SFB-TRR 161 B01 "Adaptive Self-Consistent Visualization"
Rodrigues, Nils; Schulz, Christoph; Döring, Sören; Baumgartner, Daniel; Krake, Tim; Weiskopf, Daniel, 2022, "Supplemental Material for Relaxed Dot Plots: Faithful Visualization of Samples and Their Distribution",, DaRUS, V1
Supplemental material for the paper "Relaxed Dot Plots: Faithful Visualization of Samples and Their Distribution". Contains: math behind Relaxed Dot Plots additional images pseudo-anonymous study data source code for library and test application To view the material, extract supp...
Aug 31, 2022 - SFB-TRR 161 B04 "Adaptive Algorithms for Motion Estimation"
Mehl, Lukas; Beschle, Cedric; Barth, Andrea; Bruhn, Andrés, 2022, "Replication Data for: An Anisotropic Selection Scheme for Variational Optical Flow Methods with Order-Adaptive Regularisation",, DaRUS, V1
Results of our proposed optical flow method on the Sintel and KITTI datasets. We provide the benchmark results before and after applying our refinement approach. Additionally, we provide a supplementary material to our paper with more details on the minimisation, the numerical so...
Aug 24, 2022 - SFB-TRR 161 A02 "Quantifying Visual Computing Systems"
Müller, Christoph; Heinemann, Moritz; Weiskopf, Daniel; Ertl, Thomas, 2022, "Energy consumption of scientific visualisation and data visualisation algorithms",, DaRUS, V1, UNF:6:dEyIoAgP890tWqA/WShryw== [fileUNF]
This data set comprises a series of measurements of GPU power consumption when raycasting spherical glyphs, raycasting scalar fields and when showing web-based data visualisation on Observable HQ. The data sets for sphere rendering were: pos_rad_intensity : 500000 : 0 : 10 10 10...
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