CrowdDriven: A New Challenging Dataset for Outdoor Visual Localization
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F21%3A00356149" target="_blank" >RIV/68407700:21730/21:00356149 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/ICCV48922.2021.00970" target="_blank" >https://doi.org/10.1109/ICCV48922.2021.00970</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICCV48922.2021.00970" target="_blank" >10.1109/ICCV48922.2021.00970</a>
Alternative languages
Result language
angličtina
Original language name
CrowdDriven: A New Challenging Dataset for Outdoor Visual Localization
Original language description
Visual localization is the problem of estimating the position and orientation from which a given image (or a sequence of images) is taken in a known scene. It is an important part of a wide range of computer vision and robotics applications, from self-driving cars to augmented/virtual reality systems. Visual localization techniques should work reliably and robustly under a wide range of conditions, including seasonal, weather, illumination and man-made changes. Recent benchmarking efforts model this by providing images under different conditions, and the community has made rapid progress on these datasets since their inception. However, they are limited to a few geographical regions and often recorded with a single device. We propose a new benchmark for visual localization in outdoor scenes, using crowd-sourced data to cover a wide range of geographical regions and camera devices with a focus on the failure cases of current algorithms. Experiments with state-of-the-art localization approaches show that our dataset is very challenging, with all evaluated methods failing on its hardest parts. As part of the dataset release, we provide the tooling used to generate it, enabling efficient and effective 2D correspondence annotation to obtain reference poses.
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/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)
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
ICCV2021: Proceedings of the International Conference on Computer Vision
ISBN
978-1-6654-2812-5
ISSN
1550-5499
e-ISSN
2380-7504
Number of pages
11
Pages from-to
9825-9835
Publisher name
IEEE
Place of publication
Piscataway
Event location
Montreal
Event date
Oct 11, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000798743208056