Detecting and Matching Repeated Patterns for Automatic Geo-Tagging in Urban Environments
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.
Cite
Text
Schindler et al. "Detecting and Matching Repeated Patterns for Automatic Geo-Tagging in Urban Environments." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2008. doi:10.1109/CVPR.2008.4587461Markdown
[Schindler et al. "Detecting and Matching Repeated Patterns for Automatic Geo-Tagging in Urban Environments." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2008.](https://mlanthology.org/cvpr/2008/schindler2008cvpr-detecting/) doi:10.1109/CVPR.2008.4587461BibTeX
@inproceedings{schindler2008cvpr-detecting,
title = {{Detecting and Matching Repeated Patterns for Automatic Geo-Tagging in Urban Environments}},
author = {Schindler, Grant and Krishnamurthy, Panchapagesan and Lublinerman, Roberto and Liu, Yanxi and Dellaert, Frank},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2008},
doi = {10.1109/CVPR.2008.4587461},
url = {https://mlanthology.org/cvpr/2008/schindler2008cvpr-detecting/}
}