We are building machine learning models for computer vision, natural language and robotics. In particular, we focus on learning 2D and 3D representations of objects and scenes, reconstructing geometry and materials and learning discriminative and generative models. We also investigate how complex knowledge can be incorporated into machine learning algorithms for making them robust to variations in our complex world. Applications include self-driving cars, household robots, virtual/augmented reality and scientific document analysis. We are part of the Tübingen AI Center, CyberValley, the ELLIS Institute Tübingen, the Excellence Cluster ML in Science and the CRC Robust Vision. You can follow us on Google Scholar, YouTube, Twitter, Facebook and on our Blog. Pictures from group activities can be found here and 3D reconstructions (Mip-Splattings) here and here!
Best Paper Awards: CVPR 2024, CVPR 2021, 3DV 2017, 3DV 2015, GCPR 2015
Best Paper Finalist Awards: CVPR 2019 (2x), CVPR 2013
Most influential CVPR papers: DVR #15 in 2020, OccNets #14 in 2019, KITTI #1 in 2012 (source)
Most influential ECCV papers: ConvOccNets #13 in 2022, TensoRF #2 in 2022 (source)
Awards and Prices: Sage 10-Year Impact Award 2024, Winner of nuPlan Challenge 2023, Longuet-Higgins Prize 2022, Mark Everingham Price 2021, ERC Starting Grant 2019, IEEE PAMI Young Researcher Award 2018, Heinz Maier-Leibnitz Prize 2017, German Pattern Recognition Prize 2017
Funding and Sponsors
We thank the following organizations and sponsors for supporting and funding our research.
