Location
Hackerman Hall
Abstract
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. Applications include 3D
photography, free-viewpoint television and autonomous vehicles.
Bio
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 and the IROS 2017 and ICRA 2018 Best
Paper Award Finalist. 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.
Host
Rene Vidal