# Privacy Preserving Image-Based Localization

arXiv: https://arxiv.org/abs/1903.05572 · HTML: https://mlsanity.com/paper/1903.05572/

## Key facts

- Cited by 47 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Mar 2019.

## Built on by (most cited first, 30 of 47)

- [Accelerated Coordinate Encoding: Learning to Relocalize in Minutes using RGB and Poses](https://mlsanity.com/paper/2305.14059/) (2023)
- [LaMAR: Benchmarking Localization and Mapping for Augmented Reality](https://mlsanity.com/paper/2210.10770/) (2022)
- [OrienterNet: Visual Localization in 2D Public Maps with Neural Matching](https://mlsanity.com/paper/2304.02009/) (2023)
- [An Outlook into the Future of Egocentric Vision](https://mlsanity.com/paper/2308.07123/) (2023)
- [Is Geometry Enough for Matching in Visual Localization?](https://mlsanity.com/paper/2203.12979/) (2022)
- [SNAP: Self-Supervised Neural Maps for Visual Positioning and Semantic Understanding](https://mlsanity.com/paper/2306.05407/) (2023)
- [MP-SfM: Monocular Surface Priors for Robust Structure-from-Motion](https://mlsanity.com/paper/2504.20040/) (2025)
- [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)
- [Deep Learning for Visual Localization and Mapping: A Survey](https://mlsanity.com/paper/2308.14039/) (2023)
- [LoD-Loc: Aerial Visual Localization using LoD 3D Map with Neural Wireframe Alignment](https://mlsanity.com/paper/2410.12269/) (2024)
- [NYU-VPR: Long-Term Visual Place Recognition Benchmark with View Direction and Data Anonymization Influences](https://mlsanity.com/paper/2110.09004/) (2021)
- [Fully Geometric Panoramic Localization](https://mlsanity.com/paper/2403.19904/) (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)
- [Progressive Cross-modal Knowledge Distillation for Human Action Recognition](https://mlsanity.com/paper/2208.08090/) (2022)
- [Privacy-Preserving Visual Localization with Event Cameras](https://mlsanity.com/paper/2212.03177/) (2022)
- [Obfuscation Based Privacy Preserving Representations are Recoverable Using Neighborhood Information](https://mlsanity.com/paper/2409.11536/) (2024)
- [Improved Scene Landmark Detection for Camera Localization](https://mlsanity.com/paper/2401.18083/) (2024)
- [EgoPrivacy: What Your First-Person Camera Says About You?](https://mlsanity.com/paper/2506.12258/) (2025)
- [LDL: Line Distance Functions for Panoramic Localization](https://mlsanity.com/paper/2308.13989/) (2023)
- [Active Visual Localization for Multi-Agent Collaboration: A Data-Driven Approach](https://mlsanity.com/paper/2310.02650/) (2023)
- [Representing 3D sparse map points and lines for camera relocalization](https://mlsanity.com/paper/2402.18011/) (2024)
- [Lifelong Learning and Selective Forgetting via Contrastive Strategy](https://mlsanity.com/paper/2405.18663/) (2024)
- [Reprojection Errors as Prompts for Efficient Scene Coordinate Regression](https://mlsanity.com/paper/2409.04178/) (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)
- [Remove360: Benchmarking Residuals After Object Removal in 3D Gaussian Splatting](https://mlsanity.com/paper/2508.11431/) (2025)

## Abstract (arXiv)

Image-based localization is a core component of many augmented/mixed reality (AR/MR) and autonomous robotic systems. Current localization systems rely on the persistent storage of 3D point clouds of the scene to enable camera pose estimation, but such data reveals potentially sensitive scene information. This gives rise to significant privacy risks, especially as for many applications 3D mapping is a background process that the user might not be fully aware of. We pose the following question: How can we avoid disclosing confidential information about the captured 3D scene, and yet allow reliable camera pose estimation? This paper proposes the first solution to what we call privacy preserving image-based localization. The key idea of our approach is to lift the map representation from a 3D point cloud to a 3D line cloud. This novel representation obfuscates the underlying scene geometry while providing sufficient geometric constraints to enable robust and accurate 6-DOF camera pose estimation. Extensive experiments on several datasets and localization scenarios underline the high practical relevance of our proposed approach.