# Privacy Preserving Visual SLAM

arXiv: https://arxiv.org/abs/2007.10361 · HTML: https://mlsanity.com/paper/2007.10361/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jul 2020.

## Built on by (most cited first, 15 of 15)

- [LaMAR: Benchmarking Localization and Mapping for Augmented Reality](https://mlsanity.com/paper/2210.10770/) (2022)
- [NinjaDesc: Content-Concealing Visual Descriptors via Adversarial Learning](https://mlsanity.com/paper/2112.12785/) (2021)
- [How Privacy-Preserving are Line Clouds? Recovering Scene Details from 3D Lines](https://mlsanity.com/paper/2103.05086/) (2021)
- [LoD-Loc: Aerial Visual Localization using LoD 3D Map with Neural Wireframe Alignment](https://mlsanity.com/paper/2410.12269/) (2024)
- [Analysis and Mitigations of Reverse Engineering Attacks on Local Feature Descriptors](https://mlsanity.com/paper/2105.03812/) (2021)
- [LDP-Feat: Image Features with Local Differential Privacy](https://mlsanity.com/paper/2308.11223/) (2023)
- [Gaussian Splatting Feature Fields for Privacy-Preserving Visual Localization](https://mlsanity.com/paper/2507.23569/) (2025)
- [Privacy-Preserving Representations are not Enough -- Recovering Scene Content from Camera Poses](https://mlsanity.com/paper/2305.04603/) (2023)
- [Obfuscation Based Privacy Preserving Representations are Recoverable Using Neighborhood Information](https://mlsanity.com/paper/2409.11536/) (2024)
- [A2-GNN: Angle-Annular GNN for Visual Descriptor-free Camera Relocalization](https://mlsanity.com/paper/2502.20036/) (2025)
- [Can we make NeRF-based visual localization privacy-preserving?](https://mlsanity.com/paper/2508.18971/) (2025)
- [Depth-Guided Privacy-Preserving Visual Localization Using 3D Sphere Clouds](https://mlsanity.com/paper/2605.00562/) (2026)
- [Learning to Censor by Noisy Sampling](https://mlsanity.com/paper/2203.12192/) (2022)
- [OpenFLAME: Federated Visual Positioning System to Enable Large-Scale Augmented Reality Applications](https://mlsanity.com/paper/2510.03915/) (2025)
- [GeoMix: Descriptor-Free Visual Localization via Global Context and Multi-Detector Training](https://mlsanity.com/paper/2607.02486/) (2026)

## Abstract (arXiv)

This study proposes a privacy-preserving Visual SLAM framework for estimating camera poses and performing bundle adjustment with mixed line and point clouds in real time. Previous studies have proposed localization methods to estimate a camera pose using a line-cloud map for a single image or a reconstructed point cloud. These methods offer a scene privacy protection against the inversion attacks by converting a point cloud to a line cloud, which reconstruct the scene images from the point cloud. However, they are not directly applicable to a video sequence because they do not address computational efficiency. This is a critical issue to solve for estimating camera poses and performing bundle adjustment with mixed line and point clouds in real time. Moreover, there has been no study on a method to optimize a line-cloud map of a server with a point cloud reconstructed from a client video because any observation points on the image coordinates are not available to prevent the inversion attacks, namely the reversibility of the 3D lines. The experimental results with synthetic and real data show that our Visual SLAM framework achieves the intended privacy-preserving formation and real-time performance using a line-cloud map.