Open Science

The majority of research data produced by the SFB-TRR 161 is managed in DaRUS, the data repository of the University of Stuttgart. This system is available for use by all SFB-TRR 161 researchers, regardless of their affiliation. DaRUS is inlcuded in the Registry for Research Data Repositories, and published datasets can quickly be found, either by using DaRUS's own search engine or Google's dataset search.

Dataverse of the SFB-TRR 161 on DaRUS: https://darus.uni-stuttgart.de/dataverse/TR161 

At the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2023 in Vancouver, Canada, and the International Conference on Learning Representations (ICLR) 2026 in Rio de Janeiro, Brazil, a group of our researchers presented Spring and RobustSpring — two new high-detail and high-resolution benchmarks for evaluating the accuracy and the robustness of algorithms to estimate stereo displacements (disparity), 2D motion information (optical flow) and 3D motion information (scene flow). The benchmarks are based on the Blender short movie "Spring" and depict several scenes recorded with a virtual stereo camera. The contained sequences allow the training of neural networks for stereo and motion estimation as well as the evaluation of trained neural networks to quantify (i) their qualitative performance regarding several aspects including overall performance, detailed regions, non-rigid regions, occluded regions and sky regions and (ii) their robustness under different types of image corruptions including, amongst others, noise blur, color changes and weather effects.

For more information on Spring and RobustSpring visit https://spring-benchmark.org/

In various projects, SFB-TRR 161 researchers investigate the power consumption of system components with the aim to quantify the energy cost of visualization. Several libraries that facilitate taking power measurements by various means are available as open source projects:

Power Overwhelming | Measurement framekwork for GPU power consumption

libbenchlab | A native C/C++ library for the ElmorLabs BENCHLAB telemetry system

libpowenetics | A native C/C++ library for the Powenetics v2 power measurement kit

The TRRojan is an extensible, cross-platform benchmarking framework for visual computing applications. It uses a plugin architecture to be easily extended with additional applications. The framework allows for automatically evaluating all combinations of influencing factors (rendering parameters, resolution, data set, etc.) based on a simple declarative description. The TRRojan is designed to effectively carry out quantitative benchmarks in a clean, reproducible, and easy-to-use manner.

For more information visit www.trrojan.visus.uni-stuttgart.de

A group of our scientists design prediction algorithms for the visual quality of images and videos, with respect to technical and perceptual aspects e.g. quality of experience (QoE). ​The tools of their trade include crowdsourcing, machine learning i.e. deep networks, eye-tracking. Consequently, they are creating massive multimedia databases that are suitable for training generic and accurate VQA models.

Visual Quality Databases

KoNVid-1k | Video benchmark database
KonIQ-10k | Ecologically valid image benchmark database
IQA-Experts-300 | Expert annotated ​image benchmark database
KADID-10k | Artificially distorted image database
KonPatch-30k | Patch-wise image quality assessment
IQA-Experts-300 | Expert annotated image benchmark database
KoSMo-1k | Slow-motion video database
StudyMB 2.0 | Subjective IQA of the interpolated images from Middlebury

For more information visit database.mmsp-kn.de

Project Group A

Models and Measures

Completed

Project Group B

Adaptive Algorithms

Completed

Project Group C

Interaction

Completed

Project Group D

Applications

Completed