Are Large-Scale 3D Models Really Necessary for Accurate Visual Localization?

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F17%3A00318974" target="_blank" >RIV/68407700:21730/17:00318974 - isvavai.cz</a>

Result on the web

<a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8100137" target="_blank" >http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8100137</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/CVPR.2017.654" target="_blank" >10.1109/CVPR.2017.654</a>

Result language

angličtina

Original language name

Are Large-Scale 3D Models Really Necessary for Accurate Visual Localization?

Original language description

Accurate visual localization is a key technology for autonomous navigation. 3D structure-based methods employ 3D models of the scene to estimate the full 6DOF pose of a camera very accurately. However, constructing (and extending) large-scale 3D models is still a significant challenge. In contrast, 2D image retrieval-based methods only require a database of geo-tagged images, which is trivial to construct and to maintain. They are often considered inaccurate since they only approximate the positions of the cameras. Yet, the exact camera pose can theoretically be recovered when enough relevant database images are retrieved. In this paper, we demonstrate experimentally that large-scale 3D models are not strictly necessary for accurate visual localization. We create reference poses for a large and challenging urban dataset. Using these poses, we show that combining image-based methods with local reconstructions results in a pose accuracy similar to the state-of-the-art structure-based methods. Our results suggest that we might want to reconsider the current approach for accurate large-scale localization.

Czech name

—

Czech description

—

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)

Project

<a href="/en/project/EF15_003%2F0000468" target="_blank" >EF15_003/0000468: Intelligent Machine Perception</a><br>

Continuities

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

Publication year

2017

Confidentiality

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

Article name in the collection

CVPR 2017: Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition

ISBN

978-1-5386-0457-1

ISSN

1063-6919

e-ISSN

—

Number of pages

10

Pages from-to

6175-6184

Publisher name

IEEE Computer Society Press

Place of publication

—

Event location

Honolulu

Event date

Jul 21, 2017

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000418371406029