@article{Kadosh2021, 
author = {Moti Kadosh and Yael Moses and Ariel Shamir},
title = {On the role of geometry in geo-localization},
year = {2021},
journal = {Computational Visual Media},
volume = {7},
number = {1},
pages = {103-113},
keywords = {geometry, geo-localization, CNN-based solutions, synthetic lean images},
url = {https://www.sciopen.com/article/10.1007/s41095-020-0196-2},
doi = {10.1007/s41095-020-0196-2},
abstract = {Consider the geo-localization task of finding the pose of a camera in a large 3D scene from a single image. Most existing CNN-based methods use as input textured images. We aim to experimentally explore whether texture and correlation between nearby images are necessary in a CNN-based solution for the geo-localization task. To do so, we consider lean images, textureless projections of a simple 3D model of a city. They only contain information related to the geometry of the scene viewed (edges, faces, and relative depth). The main contributions of this paper are: (i) todemonstrate the ability of CNNs to recover camera pose using lean images; and (ii) to provide insight into the role of geometry in the CNN learning process.}
}