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Privacy Preserving Localization via Coordinate Permutations
(2024)2023 IEEE/CVF International Conference on Computer Vision (ICCV)Recent methods on privacy-preserving image-based localization use a random line parameterization to protect the privacy of query images and database maps. The lifting of points to lines effectively drops one of the two geometric constraints traditionally used with point-to-point correspondences in structure-based localization. This leads to a significant loss of accuracy for the privacy-preserving methods. In this paper, we overcome this ...Conference Paper -
Privacy Preserving Localization via Coordinate Permutations
(2023)2023 IEEE/CVF International Conference on Computer Vision (ICCV)Recent methods on privacy-preserving image-based localization use a random line parameterization to protect the privacy of query images and database maps. The lifting of points to lines effectively drops one of the two geometric constraints traditionally used with point-to-point correspondences in structure-based localization. This leads to a significant loss of accuracy for the privacy-preserving methods. In this paper, we overcome this ...Conference Paper -
LaMAR: Benchmarking Localization and Mapping for Augmented Reality
(2022)Lecture Notes in Computer Science ~ Computer Vision – ECCV 2022Localization and mapping is the foundational technology for augmented reality (AR) that enables sharing and persistence of digital content in the real world. While significant progress has been made, researchers are still mostly driven by unrealistic benchmarks not representative of real-world AR scenarios. In particular, benchmarks are often based on small-scale datasets with low scene diversity, captured from stationary cameras, and ...Conference Paper -
Privacy Preserving Partial Localization
(2022)2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)Recently proposed privacy preserving solutions for cloud-based localization rely on lifting traditional point-based maps to randomized 3D line clouds. While the lifted representation is effective in concealing private information, there are two fundamental limitations. First, without careful construction of the line clouds, the representation is vulnerable to density-based inversion attacks. Secondly, after successful localization, the ...Conference Paper -
Privacy Preserving Localization and Mapping from Uncalibrated Cameras
(2021)2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)Recent works on localization and mapping from privacy preserving line features have made significant progress towards addressing the privacy concerns arising from cloud-based solutions in mixed reality and robotics. The requirement for calibrated cameras is a fundamental limitation for these approaches, which prevents their application in many crowd-sourced mapping scenarios. In this paper, we propose a solution to the uncalibrated privacy ...Conference Paper -
Privacy Preserving Structure-from-Motion
(2020)Lecture Notes in Computer Science ~ Computer Vision – ECCV 2020Over 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 ...Conference Paper