Marc Pollefeys

Swiss Federal Institute of Technology (ETH), Computer Science, Faculty Member

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Marc Pollefeys is a full professor in the Dept. of Computer Science of ETH Zurich since 2007 where he is the head of the Institute for Visual Computing and leads the Computer Vision and Geometry lab. He currently also remains associated with the Dept. of Computer Science of the University of North Carolina at Chapel Hill where he started as an assistant professor in 2002 and became an associate professor in 2005. Before this he was a postdoctoral researcher at the Katholieke Universiteit Leuven in Belgium, where he also received his M.S. and Ph.D. degrees in 1994 and 1999, respectively. His main area of research is computer vision. One of his main research goals is to develop flexible approaches to capture visual representations of real world objects, scenes and events. Dr. Pollefeys has received several prizes for his research, including a Marr prize, an NSF CAREER award, a Packard Fellowship and a European Research Council Starting Grant. He is the author or co-author of more than 100 peer-reviewed publications. He will be the general chair of the European Conference on Computer Vision in 2014 (ECCV), has been a Program Co-Chair for the IEEE Conference on Computer Vision and Pattern Recognition 2009 (CVPR), was general/program co-chair of the Third Symposium on 3D Data Processing, Visualization and Transmission and has organized workshops and courses at major vision and graphics conferences and has served on the program committees of many conferences. Prof. Pollefeys is on the editorial board of the International Journal of Computer Vision and Foundations and Trends in Computer Graphics and Computer Vision as well as several other journals and has recently served as an associate editor of the IEEE Transactions on Pattern Analysis and Machine Intelligence.
Phone: +41 44 632 31 05
Address: Institute of Visual Computing,
ETH Zurich, CNB G105,
Universitatstrasse 6,
CH-8092 Zurich, Switzerland

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Are Large-Scale 3D Models Really Necessary for Accurate Visual Localization?

HAL (Le Centre pour la Communication Scientifique Directe), Jul 21, 2017

Accurate visual localization is a key technology for autonomous navigation. 3D structure-based me... more Accurate visual localization is a key technology for autonomous navigation. 3D structure-based methods employ 3D models of the scene to estimate the full 6DOF pose of a camera very accurately. However, constructing (and extending) large-scale 3D models is still a significant challenge. In contrast, 2D image retrieval-based methods only require a database of geo-tagged images, which is trivial to construct and to maintain. They are often considered inaccurate since they only approximate the positions of the cameras. Yet, the exact camera pose can theoretically be recovered when enough relevant database images are retrieved. In this paper, we demonstrate experimentally that large-scale 3D models are not strictly necessary for accurate visual localization. We create reference poses for a large and challenging urban dataset. Using these poses, we show that combining image-based methods with local reconstructions results in a pose accuracy similar to the state-of-the-art structure-based methods. Our results suggest that we might want to reconsider the current approach for accurate large-scale localization.

Marc Pollefeys

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