Research map / Federated learning and privacy / Privacy attacks and secure learning
Privacy Preserving Localization and Mapping From Uncalibrated Cameras
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the nullspace-based rather than Cayley parameterization for the 7-line pose solver | L7Pf · Synthetic pose solver stability | runtime: 1513.1 µs -> 23.7 µs | Helped |
| Estimate focal length jointly with pose from privacy-preserving line features | Uncalibrated visual localization · Mobile Phone and HoloLens datasets | rotation and position error | No clear effect |
| Initialize maps using uncalibrated four-view line correspondences | Privacy-preserving SfM · Mobile Phone datasets | initialization success | Mixed |
| Allow each camera its own focal calibration during reconstruction | Proposed SfM · Strecha datasets | camera pose accuracy | Mixed |
| Use uncalibrated rather than known-calibration reconstruction | Privacy-preserving SfM · Internet photo collections | registered images: 750 -> 711; 810 -> 773; 377 -> 365; 608 -> 502 | Hurt |
Closest papers by meaning
- Privacy Preserving Image-Based Localization (CVPR 2019 · similarity 0.91)
- Efficient Privacy-Preserving Visual Localization Using 3D Ray Clouds (CVPR 2024 · similarity 0.85)
- Gaussian Splatting Feature Fields for (Privacy-Preserving) Visual Localization (CVPR 2025 · similarity 0.82)
- Validating Privacy-Preserving Face Recognition under a Minimum Assumption (CVPR 2024 · similarity 0.80)
- CroCoDL: Cross-device Collaborative Dataset for Localization (CVPR 2025 · similarity 0.80)
- VGGT-SLAM: Dense RGB SLAM Optimized on the SL(4) Manifold (NeurIPS 2025 · similarity 0.79)
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