All

What are you looking for?

All
Projects
Results
Organizations

Quick search

  • Projects supported by TA ČR
  • Excellent projects
  • Projects with the highest public support
  • Current projects

Smart search

  • That is how I find a specific +word
  • That is how I leave the -word out of the results
  • “That is how I can find the whole phrase”

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

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • 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

  • e-ISSN

  • 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