CrossLocate: Cross-Modal Large-Scale Visual Geo-Localization in Natural Environments using Rendered Modalities

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F22%3APU143327" target="_blank" >RIV/00216305:26230/22:PU143327 - isvavai.cz</a>

Result on the web

<a href="https://openaccess.thecvf.com/content/WACV2022/html/Tomesek_CrossLocate_Cross-Modal_Large-Scale_Visual_Geo-Localization_in_Natural_Environments_Using_Rendered_WACV_2022_paper.html" target="_blank" >https://openaccess.thecvf.com/content/WACV2022/html/Tomesek_CrossLocate_Cross-Modal_Large-Scale_Visual_Geo-Localization_in_Natural_Environments_Using_Rendered_WACV_2022_paper.html</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/WACV51458.2022.00225" target="_blank" >10.1109/WACV51458.2022.00225</a>

Result language

angličtina

Original language name

CrossLocate: Cross-Modal Large-Scale Visual Geo-Localization in Natural Environments using Rendered Modalities

Original language description

We propose a novel approach to visual geo-localization in natural environments. This is a challenging problem due to vast localization areas, the variable appearance of outdoor environments and the scarcity of available data. In order to make the research of new approaches possible, we first create two databases containing "synthetic" images of various modalities. These image modalities are rendered from a 3D terrain model and include semantic segmentations, silhouette maps and depth maps. By combining the rendered database views with existing datasets of photographs (used as "queries" to be localized), we create a unique benchmark for visual geo-localization in natural environments, which contains correspondences between query photographs and rendered database imagery. The distinct ability to match photographs to synthetically rendered databases defines our task as "cross-modal". On top of this benchmark, we provide thorough ablation studies analysing the localization potential of the database image modalities. We reveal the depth information as the best choice for outdoor localization. Finally, based on our observations, we carefully develop a fully-automatic method for large-scale cross-modal localization using image retrieval. We demonstrate its localization performance outdoors in the entire state of Switzerland. Our method reveals a large gap between operating within a single image domain (e.g. photographs) and working across domains (e.g. photographs matched to rendered images), as gained knowledge is not transferable between the two. Moreover, we show that modern localization methods fail when applied to such a cross-modal task and that our method achieves significantly better results than state-of-the-art approaches. The datasets, code and trained models are available on the project website: http://cphoto.fit.vutbr.cz/crosslocate/.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/LTAIZ19004" target="_blank" >LTAIZ19004: Deep-Learning Approach to Topographical Image Analysis</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

ISBN

978-1-6654-0477-8

ISSN

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e-ISSN

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Number of pages

10

Pages from-to

3174-3183

Publisher name

Institute of Electrical and Electronics Engineers

Place of publication

Waikoloa

Event location

Waikoloa, Hawaii

Event date

Jan 4, 2022

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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