Computer Vision and Learning Lab
The Computer Vision and Learning Lab works in the field of Computer Vision and Machine Learning. The Lab has three groups: 3D Computer Vision (Carsten Rother), Explainable Machine Learning (Ullrich Köthe), and Optimization for Machine Learning (Bogdan Savchynskyy). Carsten Rother is head of the Lab. We work on a large range of research topics, such as 3D reconstruction, Image synthesis, Invertible Neural Networks, Explainable and Trustworthy Machine Learning, Large-scale discrete optimization, assignment and tracking. We collaborate with researchers from many other disciplines, such as computational biology, medicine, astronomy, or environmental physics. We have also been the steppingstone for successful start-ups.
- Carsten Rother was selected by aminer to be among the 9 most influential Computer Vision researchers in Europe. See: AI …
- S.K. Mustikovela, V. Jampani, S. De Mello, S. Liu, U. Iqbal, C. Rother, J. Kautz “Self-Supervised Viewpoint Learning from Image …
- Ullrich Köthe, Markus Brubaeker (York University/Toronto) and Carsten Rother offer a half-day tutorial at ECCV 2020 in Glasgow on “Normalizing Flows …
- Learning to Think Outside the Box: Wide-Baseline Light Field Depth Estimation with EPI-Shift [pdf] [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 have also been collaborating with various industrial research labs - such as Microsoft Research Cambridge, Bejing and Redmond, Daimler, and Facebook Artificial Intelligence Researchers (FAIR).
We are part of the Heidelberg HCI 3rd phase, where we collaborate closely with Bosch.