PAIGE: PAirwise Image Geometry Encoding for Improved Efficiency in Structure-from-Motion
Abstract
Large-scale Structure-from-Motion systems typically spend major computational effort on pairwise image matching and geometric verification in order to discover connected components in large-scale, unordered image collections. In recent years, the research community has spent significant effort on improving the efficiency of this stage. In this paper, we present a comprehensive overview of various state-of-the-art methods, evaluating and analyzing their performance. Based on the insights of this evaluation, we propose a learning-based approach, the PAirwise Image Geometry Encoding (PAIGE), to efficiently identify image pairs with scene overlap without the need to perform exhaustive putative matching and geometric verification. PAIGE achieves state-of-the-art performance and integrates well into existing Structure-from-Motion pipelines.
Cite
Text
Schonberger et al. "PAIGE: PAirwise Image Geometry Encoding for Improved Efficiency in Structure-from-Motion." Conference on Computer Vision and Pattern Recognition, 2015. doi:10.1109/CVPR.2015.7298703Markdown
[Schonberger et al. "PAIGE: PAirwise Image Geometry Encoding for Improved Efficiency in Structure-from-Motion." Conference on Computer Vision and Pattern Recognition, 2015.](https://mlanthology.org/cvpr/2015/schonberger2015cvpr-paige/) doi:10.1109/CVPR.2015.7298703BibTeX
@inproceedings{schonberger2015cvpr-paige,
title = {{PAIGE: PAirwise Image Geometry Encoding for Improved Efficiency in Structure-from-Motion}},
author = {Schonberger, Johannes L. and Berg, Alexander C. and Frahm, Jan-Michael},
booktitle = {Conference on Computer Vision and Pattern Recognition},
year = {2015},
doi = {10.1109/CVPR.2015.7298703},
url = {https://mlanthology.org/cvpr/2015/schonberger2015cvpr-paige/}
}