Minimal Solvers for 3D Geometry from Satellite Imagery
Abstract
We propose two novel minimal solvers which advance the state of the art in satellite imagery processing. Our methods are efficient and do not rely on the prior existence of complex inverse mapping functions to correlate 2D image coordinates and 3D terrain. Our first solver improves on the stereo correspondence problem for satellite imagery, in that we provide an exact image-to-object space mapping (where prior methods were inaccurate). Our second solver provides a novel mechanism for 3D point triangulation, which has improved robustness and accuracy over prior techniques. Given the usefulness and ubiquity of satellite imagery, our proposed methods allow for improved results in a variety of existing and future applications.
Cite
Text
Zheng et al. "Minimal Solvers for 3D Geometry from Satellite Imagery." International Conference on Computer Vision, 2015. doi:10.1109/ICCV.2015.91Markdown
[Zheng et al. "Minimal Solvers for 3D Geometry from Satellite Imagery." International Conference on Computer Vision, 2015.](https://mlanthology.org/iccv/2015/zheng2015iccv-minimal/) doi:10.1109/ICCV.2015.91BibTeX
@inproceedings{zheng2015iccv-minimal,
title = {{Minimal Solvers for 3D Geometry from Satellite Imagery}},
author = {Zheng, Enliang and Wang, Ke and Dunn, Enrique and Frahm, Jan-Michael},
booktitle = {International Conference on Computer Vision},
year = {2015},
doi = {10.1109/ICCV.2015.91},
url = {https://mlanthology.org/iccv/2015/zheng2015iccv-minimal/}
}