LandscapeAR: Large Scale Outdoor Augmented Reality by Matching Photographs with Terrain Models Using Learned Descriptors
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F20%3APU138874" target="_blank" >RIV/00216305:26230/20:PU138874 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/chapter/10.1007/978-3-030-58526-6_18" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-030-58526-6_18</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-58526-6_18" target="_blank" >10.1007/978-3-030-58526-6_18</a>
Alternative languages
Result language
angličtina
Original language name
LandscapeAR: Large Scale Outdoor Augmented Reality by Matching Photographs with Terrain Models Using Learned Descriptors
Original language description
We introduce a solution to large scale Augmented Reality for outdoor scenes by registering camera images to textured Digital Elevation Models (DEMs). To accommodate the inherent differences in appearance between real images and DEMs, we train a cross-domain feature descriptor using Structure From Motion (SFM) guided reconstructions to acquire training data. Our method runs efficiently on a mobile device and outperforms existing learned and hand-designed feature descriptors for this task.
Czech name
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Czech description
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Classification
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)
Result continuities
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)
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Computer Vision - ECCV 2020
ISBN
978-3-030-58525-9
ISSN
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e-ISSN
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Number of pages
18
Pages from-to
295-312
Publisher name
Springer Nature Switzerland AG
Place of publication
Cham
Event location
online
Event date
Aug 23, 2020
Type of event by nationality
EUR - Evropská akce
UT code for WoS article
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