The focus of our team lies on 3D reconstruction and analysis of general deformable scenes, 3D reconstruction of the human body and matching problems on point sets and graphs. We are interested in neural approaches (both supervised and unsupervised), physics-based methods as well as new hardware and sensors (e.g., quantum computers and event cameras).
Many research questions at the intersection of computer graphics, computer vision and machine learning involve challenging search problems (e.g., graph matching) or the optimisation of non-convex objectives. For such problems, we develop new algorithmic formulations that can be solved on modern adiabatic quantum annealers or universal quantum computers and investigate which advantages these approaches offer compared to existing classical methods.
Our reserach interests include (but are not limited to):
- 3D Reconstruction and Tracking of Rigid and Non-Rigid Scenes and Objects
- Neural Rendering
- Point Set Registration and Matching Problems
- Quantum Algorithms for Computer Vision and Graphics
- Event-based Approaches in Vision and Graphics
- 08.09.2021: Dennis Willsch gave a 4DQV Lecture Series talk titled Applications on Quantum Annealers at Jülich Supercomputing Centre. The video can be found here.
- 19.08.2021: We are going to present the following works at ICCV 2021: GraviCap, Non-Rigid Neural Radiance Fields, Iterative Shape Matching via Quantum Annealing and EventHands.
- 05.07.2021: The source code of PhysCap is now available here.