Detecting and matching repeated patterns for automatic geo-tagging in urban environments

We present a novel method for automatically geo-tagging photographs of man-made environments via detection and matching of repeated patterns. Highly repetitive environments introduce numerous correspondence ambiguities and are problematic for traditional wide-baseline matching methods. Our method exploits the highly repetitive nature of urban environments, detecting multiple perspectively distorted periodic 2D patterns in an image and matching them to a 3D database of textured facades by reasoning about the underlying canonical forms of each pattern. Multiple 2D-to-3D pattern correspondences enable robust recovery of camera orientation and location. We demonstrate the success of this method in a large urban environment.

• Type: Conference
• Year: 2008
• Where: CVPR
• Authors: Grant Schindler, Panchapagesan Krishnamurthy, Roberto Lublinerman, Yanxi Liu, Frank Dellaert
• Keywords: Computer Vision, CVPR 2008, Numerous Correspondence Ambiguities, Periodic 2d Patterns, Repetitive Environments, Urban Environments, Wide-baseline Matching Methods