How Privacy-Preserving are Line Clouds? Recovering Scene Details from 3D Lines
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F21%3A00356124" target="_blank" >RIV/68407700:21730/21:00356124 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/CVPR46437.2021.01541" target="_blank" >https://doi.org/10.1109/CVPR46437.2021.01541</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/CVPR46437.2021.01541" target="_blank" >10.1109/CVPR46437.2021.01541</a>
Alternative languages
Result language
angličtina
Original language name
How Privacy-Preserving are Line Clouds? Recovering Scene Details from 3D Lines
Original language description
Visual localization is the problem of estimating the camera pose of a given image with respect to a known scene. Visual localization algorithms are a fundamental building block in advanced computer vision applications, including Mixed and Virtual Reality systems. Many algorithms used in practice represent the scene through a Structure-from-Motion (SfM) point cloud and use 2D-3D matches between a query image and the 3D points for camera pose estimation. As recently shown, image details can be accurately recovered from SfM point clouds by translating renderings of the sparse point clouds to images. To address the resulting potential privacy risks for user-generated content, it was recently proposed to lift point clouds to line clouds by replacing 3D points by randomly oriented 3D lines passing through these points. The resulting representation is unintelligible to humans and effectively prevents point cloudto-image translation. This paper shows that a significant amount of information about the 3D scene geometry is preserved in these line clouds, allowing us to (approximately) recover the 3D point positions and thus to (approximately) recover image content. Our approach is based on the observation that the closest points between lines can yield a good approximation to the original 3D points.
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
Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
ISBN
978-1-6654-4510-8
ISSN
1063-6919
e-ISSN
2575-7075
Number of pages
11
Pages from-to
15663-15673
Publisher name
IEEE Computer Society
Place of publication
USA
Event location
Nashville
Event date
Jun 20, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000742075005087