Investigating the Role of Image Retrieval for Visual Localization
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F22%3A00364120" target="_blank" >RIV/68407700:21730/22:00364120 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1007/s11263-022-01615-7" target="_blank" >https://doi.org/10.1007/s11263-022-01615-7</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s11263-022-01615-7" target="_blank" >10.1007/s11263-022-01615-7</a>
Alternative languages
Result language
angličtina
Original language name
Investigating the Role of Image Retrieval for Visual Localization
Original language description
Visual localization, i.e., camera pose estimation in a known scene, is a core component of technologies such as autonomous driving and augmented reality. State-of-the-art localization approaches often rely on image retrieval techniques for one of two purposes: (1) provide an approximate pose estimate or (2) determine which parts of the scene are potentially visible in a given query image. It is common practice to use state-of-the-art image retrieval algorithms for both of them. These algorithms are often trained for the goal of retrieving the same landmark under a large range of viewpoint changes which often differs from the requirements of visual localization. In order to investigate the consequences for visual localization, this paper focuses on understanding the role of image retrieval for multiple visual localization paradigms. First, we introduce a novel benchmark setup and compare state-of-the-art retrieval representations on multiple datasets using localization performance as metric. Second, we investigate several definitions of “ground truth” for image retrieval. Using these definitions as upper bounds for the visual localization paradigms, we show that there is still significant room for improvement. Third, using these tools and in-depth analysis, we show that retrieval performance on classical landmark retrieval or place recognition tasks correlates only for some but not all paradigms to localization performance. Finally, we analyze the effects of blur and dynamic scenes in the images. We conclude that there is a need for retrieval approaches specifically designed for localization paradigms. Our benchmark and evaluation protocols are available at https://github.com/naver/kapture-localization.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
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
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
International Journal of Computer Vision
ISSN
0920-5691
e-ISSN
1573-1405
Volume of the periodical
130
Issue of the periodical within the volume
July
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
26
Pages from-to
1811-1836
UT code for WoS article
000802316600003
EID of the result in the Scopus database
2-s2.0-85130728154