Privacy preserving structure-from-motion
(2020) 16th European Conference on Computer Vision, ECCV 2020 In Lecture Notes in Computer Science 12346. p.333-350- Abstract
- Over the last years, visual localization and mapping solutions have been adopted by an increasing number of mixed reality and robotics systems. The recent trend towards cloud-based localization and mapping systems has raised significant privacy concerns. These are mainly grounded by the fact that these services require users to upload visual data to their servers, which can reveal potentially confidential information, even if only derived image features are uploaded. Recent research addresses some of these concerns for the task of image-based localization by concealing the geometry of the query images and database maps. The core idea of the approach is to lift 2D/3D feature points to random lines, while still providing sufficient... (More)
- Over the last years, visual localization and mapping solutions have been adopted by an increasing number of mixed reality and robotics systems. The recent trend towards cloud-based localization and mapping systems has raised significant privacy concerns. These are mainly grounded by the fact that these services require users to upload visual data to their servers, which can reveal potentially confidential information, even if only derived image features are uploaded. Recent research addresses some of these concerns for the task of image-based localization by concealing the geometry of the query images and database maps. The core idea of the approach is to lift 2D/3D feature points to random lines, while still providing sufficient constraints for camera pose estimation. In this paper, we further build upon this idea and propose solutions to the different core algorithms of an incremental Structure-from-Motion pipeline based on random line features. With this work, we make another fundamental step towards enabling privacy preserving cloud-based mapping solutions. Various experiments on challenging real-world datasets demonstrate the practicality of our approach achieving comparable results to standard Structure-from-Motion systems. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/69f1f3f6-3944-419e-b61e-1108d21e387e
- author
- Geppert, Marcel ; Larsson, Viktor LU ; Speciale, Pablo ; Schönberger, Johannes L and Pollefeys, Marc
- publishing date
- 2020
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- Computer Vision – ECCV 2020 : 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part I - 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part I
- series title
- Lecture Notes in Computer Science
- volume
- 12346
- pages
- 18 pages
- publisher
- Springer
- conference name
- 16th European Conference on Computer Vision, ECCV 2020
- conference location
- Glasgow, United Kingdom
- conference dates
- 2020-08-23 - 2020-08-28
- external identifiers
-
- scopus:85097247202
- ISSN
- 1611-3349
- 0302-9743
- ISBN
- 978-3-030-58451-1
- 978-3-030-58452-8
- DOI
- 10.1007/978-3-030-58452-8_20
- language
- English
- LU publication?
- no
- id
- 69f1f3f6-3944-419e-b61e-1108d21e387e
- date added to LUP
- 2022-09-06 13:15:12
- date last changed
- 2024-04-18 14:36:54
@inproceedings{69f1f3f6-3944-419e-b61e-1108d21e387e,
abstract = {{Over the last years, visual localization and mapping solutions have been adopted by an increasing number of mixed reality and robotics systems. The recent trend towards cloud-based localization and mapping systems has raised significant privacy concerns. These are mainly grounded by the fact that these services require users to upload visual data to their servers, which can reveal potentially confidential information, even if only derived image features are uploaded. Recent research addresses some of these concerns for the task of image-based localization by concealing the geometry of the query images and database maps. The core idea of the approach is to lift 2D/3D feature points to random lines, while still providing sufficient constraints for camera pose estimation. In this paper, we further build upon this idea and propose solutions to the different core algorithms of an incremental Structure-from-Motion pipeline based on random line features. With this work, we make another fundamental step towards enabling privacy preserving cloud-based mapping solutions. Various experiments on challenging real-world datasets demonstrate the practicality of our approach achieving comparable results to standard Structure-from-Motion systems.}},
author = {{Geppert, Marcel and Larsson, Viktor and Speciale, Pablo and Schönberger, Johannes L and Pollefeys, Marc}},
booktitle = {{Computer Vision – ECCV 2020 : 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part I}},
isbn = {{978-3-030-58451-1}},
issn = {{1611-3349}},
language = {{eng}},
pages = {{333--350}},
publisher = {{Springer}},
series = {{Lecture Notes in Computer Science}},
title = {{Privacy preserving structure-from-motion}},
url = {{http://dx.doi.org/10.1007/978-3-030-58452-8_20}},
doi = {{10.1007/978-3-030-58452-8_20}},
volume = {{12346}},
year = {{2020}},
}