Daniel Cremers studied physics and mathematics at Heidelberg University, Indiana State and Stony Brook. He awarded a doctorate in computer science in 2002 at the University of Mannheim. Following this, he worked as a postdoctoral researcher at UCLA. In 2004, he joined Siemens Corporate Research (Princeton) as a member of staff. In 2005, he accepted an appointment to a professorship at the University of Bonn. Professor Cremers has been full professor of computer vision and artificial intelligence at TUM since 2009. He has served on the editorial boards of the International Journal of Computer Vision, IEEE Transactions on Pattern Recognition and Machine Intelligence (until 2014), the SIAM Journal of Imaging Sciences and the Dagstuhl Open Access Series in Informatics (OASIcs).
2016 Gottfried Wilhelm Leibniz-Preis der DFG
2016 Best Paper Award, Symposium on Graphics Processing
2014 European Research Council Consolidator Grant
2009 European Research Council Starting Grant
2005 Emmy Noether Scholarship
2005 UCLA Chancellor’s Award for Postdoctoral Research
2004 Olympuspreis der Deutschen Arbeitsgemeinschaft für Mustererkennung
2003Best Paper of the Year, Int. Pattern Recognition Society
Daniel Cremers conducts research on computer vision, machine learning, robotics and optimization. The primary objective of this research is to improve the ability of machines to analyze and interpret image data. His research focuses on convex optimization methods, partial differential equations, graph theoretic algorithms, deep learning and statistical inference.
- Non-Rigid Puzzles. Computer Graphics Forum 35 (5), 2016, 135-143.
- Convex Relaxation of Vectorial Problems with Coupled Regularization. SIAM Journal on Imaging Sciences 7 (1), 2014, 294-336.
- A Super-Resolution Framework for High-Accuracy Multiview Reconstruction. International Journal of Computer Vision 106 (2), 2013, 172-191.
- A Convex Approach to Minimal Partitions. SIAM Journal on Imaging Sciences 5 (4), 2012, 1113-1158.
- Multiview Stereo and Silhouette Consistency via Convex Functionals over Convex Domains. IEEE Transactions on Pattern Analysis and Machine Intelligence 33 (6), 2011, 1161-1174.
- A Combinatorial Solution for Model-Based Image Segmentation and Real-Time Tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 32 (7), 2010, 1153-1164.
- Global Solutions of Variational Models with Convex Regularization. SIAM Journal on Imaging Sciences 3 (4), 2010, 1122-1145.
- Dynamical statistical shape priors for level set-based tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 28 (8), 2006, 1262-1273.
- Kernel Density Estimation and Intrinsic Alignment for Shape Priors in Level Set Segmentation. International Journal of Computer Vision 69 (3), 2006, 335-351.