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Carnegie Mellon Robotics Institute

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Abstract

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.

Notes

Associated Center(s) / Consortia: Vision and Autonomous Systems Center

Associated Lab(s) / Group(s): Computational Symmetry

Associated Project(s): A Computational Model for Repeated Pattern Perception using Crystallographic Groups

Note: (to appear)

Text Reference

Grant Schindler, Panchapagesan Krishnamurthy, Roberto Lublinerman, Yanxi Liu, and Frank Dellaert, "Detecting and Matching Repeated Patterns for Automatic Geo-tagging in Urban Environments," Proceedings of CVPR 2008, June, 2008.

BibTeX Reference

@inproceedings{Liu_2008_6047,
   author = "Grant Schindler and Panchapagesan Krishnamurthy and Roberto Lublinerman and Yanxi Liu and Frank Dellaert",
   title = "Detecting and Matching Repeated Patterns for Automatic Geo-tagging in Urban Environments",
   booktitle = "Proceedings of CVPR 2008",
   month = "June",
   year = "2008",
   Notes = "(to appear)"
}

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