Seminar by Prof Konrad Schindler from ETH Zurich – Friday 7th September 2012

This Friday, 7th September 2012, we welcome Prof Konrad Schindler from ETH Zurich. He will be presenting the below seminar at 3.10pm in Room 5.57.

TITLE: Monocular 3D Estimation With Deformable Object Models

ABSTRACT :Monocular 3D reconstruction is a geometrically ill-defined problem. Still, it can be accomplished when prior knowledge about the observed objects is available. We revisit ideas from the early days of computer vision, namely, 3D geometric representations of semantically defined object categories. These representations can recover detailed geometric object hypotheses, including the relative 3D positions of object parts. In combination with recent robust techniques for local shape description and inference, such representations can be applied to real-world images. We analyze this approach in detail, and demonstrate novel applications enabled by the geometric object class representation, such as fine-grained categorization of cars according to their 3D geometry, and ultra-wide baseline matching.

BIO: Konrad Schindler received a Diplomingenieur ( degree in photogrammetry from Vienna University of Technology, Austria in 1999, and a PhD in computer science from Graz University of Technology, Austria, in 2003. He has worked as a photogrammetric engineer in the private industry, and held researcher positions in the Computer Graphics and Vision Department of Graz University of Technology, the Digital Perception Lab of Monash University, and the Computer Vision Lab of ETH Zurich. He became assistant professor of Image Understanding at TU Darmstadt in 2009, and since 2010 has been a tenured professor of Photogrammetry and Remote Sensing at ETH Zurich. His research interests lie in the field of computer vision, photogrammetry, and remote sensing. He currently serves as head of the institute of Geodesy and Photogrammetry, as president of ISPRS commission 3, and as associate editor for the ISPRS Journal of Photogrammetry and Remote Sensing, and for the Image and Vision Computing Journal.

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