Welcome to the Computer Vision Lab Dresden
We welcome Benjamin Guthier as head of the CVLD starting 15.09.2017.
Carsten Rother's group moved 403 kilometers to the south/west (Heidelberg) on 31.08.2017,
and is now called VLL --- Visual Learning Lab.
Information for TUD Students: Next dates for oral exams of Carsten Rother lectures (CV1 and CV2) are 2.8.2017 and 2.11.2017. There will be likely no other exam dates in 2017.
Computer Vision is a science that develops models and methods for understanding, analyzing, acquiring and processing images and generally high-dimensional structured data. Computer Vision is an inter-disciplinary field with strong links to Machine Learning, Optimization, Biology, Computer Graphics, and Human Computer Interaction. The mission of the Computer Vision Lab Dresden is to develop novel theoretical concepts which are practically relevant. We work on a broad range of application areas, from image matching, via semantic scene understanding, to Bio Imaging. On the theoretical side we focus on optimization and learning in probabilistic graphical models, as well as Deep Learning.
- Oliver Groth has won the Carl Zeiss Diplompreis for his Diplom thesis on the topic “Visual Phrase Grounding with Variable …
- We are co-organizing the 3rd International Workshop on Recovering 6D Object Pose in conjuction with ICCV 2017, Venice. Part of …
- Seven papers accepted to CVPR 17. One paper accepted to ISBI 2017: Using noisy crowd sourcing data to segment the …
- Jakob Kruse has won the Carl Zeiss Diplompreis for his Master’s thesis on the topic “Comparison of Learned Inference Approaches …
- Ever wondered how to train a computer vision pipeline, which contains RANSAC, in an end-to-end fashion? See our project page …
The Rich Scene Model (ERC Consolidator Grant)
Given a sequence of images the goal is to recover a rich, detailed representation of the 3D world, ranging from physical to semantical aspects. To achieve this we investigate new ways to combine feature learning, modelling, physical laws, and optimization in large-scale discrete-continuous-valued probabilistic graphical model.
Collaborators & Industrial Partners
We also have industrial collaborations with Daimler Research, Adobe (Seattle) and Basler AG.