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Research Article | Open Access

On the role of geometry in geo-localization

Department of Electrical Engineering, Tel-Aviv University, Tel-Aviv, Israel
The Efi Arazi School of Computer Science, The Interdisciplinary Center Herzliya, Herzliya, Israel
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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.

Keywords

geo-localizationgeometryCNN-based solutionssynthetic lean images

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Computational Visual Media
Volume 7 Issue 1,
March 2021
Pages 103-113
DOI: 10.1007/s41095-020-0196-2
Cite this article:
Kadosh M, Moses Y, Shamir A. On the role of geometry in geo-localization. Computational Visual Media, 2021, 7(1): 103-113. https://doi.org/10.1007/s41095-020-0196-2

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Received: 18 July 2020
Accepted: 12 September 2020
Published: 07 January 2021
© The Author(s) 2020

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