The reconstruction of the 3D world from images is among the central challenges in computer vision. Starting in the 2000s, researchers have pioneered algorithms which can reconstruct camera motion and sparse feature-points in real-time. In my talk, I will introduce direct methods for camera tracking and 3D reconstruction which do not require feature point estimation, which exploit all available input data and which recover dense or semi-dense geometry rather than sparse point clouds. They lead to a drastic boost in precision and robustness. Furthermore, I will showcase some applications ranging from 3D photography and 3D television to autonomous navigation.
Daniel Cremers received Bachelor degrees in Mathematics (1994) and Physics (1994), and a Master’s degree in Theoretical Physics (1997) from the University of Heidelberg. In 2002 he obtained a PhD in Computer Science from the University of Mannheim, Germany.
Subsequently he spent two years as a postdoctoral researcher at the University of California at Los Angeles and one year as a permanent researcher at Siemens Corporate Research in Princeton. From 2005 until 2009 he was associate professor at the University of Bonn, Germany. Since 2009 he holds the Chair of Computer Vision and Artificial Intelligence at the Technical University, Munich. He has coauthored over 300 publications which received numerous awards, most recently the SGP 2016 Best Paper Award, the CVPR 2016 Best Paper Honorable Mention, the IROS 2017, ICRA 2018 Best Paper Award Finalist and the 3DV 2018 Best Paper Award. For pioneering research he received a Starting Grant (2009), a Proof of Concept Grant (2014) and a Consolidator Grant (2015) from the European Research Council. In December 2010 he was listed among “Germany’s top 40 researchers below 40” (Capital). Prof. Cremers received the Gottfried-Wilhelm Leibniz Award 2016, the most important research award in German academia.