SegLoc: Learning Segmentation-Based Representations for Privacy-Preserving Visual Localization
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F23%3A00372022" target="_blank" >RIV/68407700:21230/23:00372022 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21730/23:00372022
Result on the web
<a href="https://doi.org/10.1109/CVPR52729.2023.01476" target="_blank" >https://doi.org/10.1109/CVPR52729.2023.01476</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/CVPR52729.2023.01476" target="_blank" >10.1109/CVPR52729.2023.01476</a>
Alternative languages
Result language
angličtina
Original language name
SegLoc: Learning Segmentation-Based Representations for Privacy-Preserving Visual Localization
Original language description
Inspired by properties of semantic segmentation, in this paper we investigate how to leverage robust image segmentation in the context of privacy-preserving visual localization. We propose a new localization framework, SegLoc, that leverages image segmentation to create robust, compact, and privacy-preserving scene representations, i.e., 3D maps. We build upon the correspondence-supervised, fine-grained segmentation approach from [42], making it more robust by learning a set of cluster labels with discriminative clustering, additional consistency regularization terms and we jointly learn a global image representation along with a dense local representation. In our localization pipeline, the former will be used for retrieving the most similar images, the latter to refine the retrieved poses by minimizing the label inconsistency between the 3D points of the map and their projection onto the query image. In various experiments, we show that our proposed representation allows to achieve (close-to) state-of-the-art pose estimation results while only using a compact 3D map that does not contain enough information about the original images for an attacker to reconstruct personal information.
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
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
ISBN
979-8-3503-0129-8
ISSN
1063-6919
e-ISSN
2575-7075
Number of pages
12
Pages from-to
15380-15391
Publisher name
IEEE Computer Society
Place of publication
USA
Event location
Vancouver
Event date
Jun 18, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001062522107067