Přístupnostní navigace
E-application
Search Search Close
Publication detail
BREJCHA, J. LUKÁČ, M. HOLD-GEOFFROY, Y. WANG, O. ČADÍK, M.
Original Title
LandscapeAR: Large Scale Outdoor Augmented Reality by Matching Photographs with Terrain Models Using Learned Descriptors
Type
conference paper
Language
English
Original 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
Authors
BREJCHA, J.; LUKÁČ, M.; HOLD-GEOFFROY, Y.; WANG, O.; ČADÍK, M.
Released
17. 8. 2020
Publisher
Springer Nature Switzerland AG
Location
Cham
ISBN
978-3-030-58525-9
Book
Computer Vision - ECCV 2020
Edition
Lecture Notes in Computer Science
Pages from
295
Pages to
312
Pages count
18
URL
https://link.springer.com/chapter/10.1007/978-3-030-58526-6_18
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" }