Result Details

LandscapeAR: Large Scale Outdoor Augmented Reality by Matching Photographs with Terrain Models Using Learned Descriptors

BREJCHA, J.; LUKÁČ, M.; HOLD-GEOFFROY, Y.; WANG, O.; ČADÍK, M. LandscapeAR: Large Scale Outdoor Augmented Reality by Matching Photographs with Terrain Models Using Learned Descriptors. In Computer Vision - ECCV 2020. Lecture Notes in Computer Science. Cham: Springer Nature Switzerland AG, 2020. p. 295-312. ISBN: 978-3-030-58525-9.
Type
conference paper
Language
English
Authors
Brejcha Jan, Ing., Ph.D., DCGM (FIT)
Lukáč Michal, FIT (FIT)
Hold-Geoffroy Yannick
Wang Oliver
Čadík Martin, doc. Ing., Ph.D., DCGM (FIT)
Abstract

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.

Keywords

augmented reality, descriptor matching, cross domain matching, camera calibration, visual localization, structure-from-motion, terrain model, digital elevation model, photograph, computational photography

URL
Published
2020
Pages
295–312
Proceedings
Computer Vision - ECCV 2020
Series
Lecture Notes in Computer Science
Volume
12374
Conference
European Conference on Computer Vision 2020
ISBN
978-3-030-58525-9
Publisher
Springer Nature Switzerland AG
Place
Cham
DOI
10.1007/978-3-030-58526-6_18
EID Scopus
2-s2.0-85093082471
BibTeX 
@inproceedings{BUT168487,
  author="Jan {Brejcha} and Michal {Lukáč} and Yannick {Hold-Geoffroy} and Oliver {Wang} and Martin {Čadík}",
  title="LandscapeAR: Large Scale Outdoor Augmented Reality by Matching Photographs with Terrain Models Using Learned Descriptors",
  booktitle="Computer Vision - ECCV 2020",
  year="2020",
  series="Lecture Notes in Computer Science",
  volume="12374",
  pages="295--312",
  publisher="Springer Nature Switzerland AG",
  address="Cham",
  doi="10.1007/978-3-030-58526-6\{_}18",
  isbn="978-3-030-58525-9",
  url="https://link.springer.com/chapter/10.1007/978-3-030-58526-6_18"
}
Projects
Deep-Learning Approach to Topographical Image Analysis, MŠMT, INTER-EXCELLENCE - Podprogram INTER-ACTION, LTAIZ19004, start: 2019-07-01, end: 2022-06-30, completed
Moderní metody zpracování, analýzy a zobrazování multimediálních a 3D dat, BUT, Vnitřní projekty VUT, FIT-S-20-6460, start: 2020-03-01, end: 2023-02-28, completed
Research groups
Computational Photography Group - CPhoto@FIT (RG CPHOTO)
Departments
Department of Computer Graphics and Multimedia (DCGM)
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