Research map / Point clouds / Point cloud classification
Privacy Preserving Image-Based Localization
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Replace mapped 3D points with randomly directed 3D lines | Image-based localization · 15 real-world datasets | pose accuracy/recall | Mixed |
| Use known structure, gravity, and scale where available | Traditional and privacy-preserving localization variants · 15 real-world datasets | pose accuracy/recall | Helped |
| Use 3D line-cloud maps instead of 3D point-cloud maps | Image-based localization · 15 real-world datasets | minimal solver time | Hurt |
| Retain only every 20th point or line in the map | Point-cloud and line-cloud localization · 15 real-world datasets | pose estimation robustness | No clear effect |
Closest papers by meaning
- Privacy-Preserving Representations Are Not Enough: Recovering Scene Content From Camera Poses (CVPR 2023 · similarity 0.92)
- Privacy Preserving Partial Localization (CVPR 2022 · similarity 0.91)
- Privacy Preserving Localization and Mapping From Uncalibrated Cameras (CVPR 2021 · similarity 0.91)
- Efficient Privacy-Preserving Visual Localization Using 3D Ray Clouds (CVPR 2024 · similarity 0.89)
- Gaussian Splatting Feature Fields for (Privacy-Preserving) Visual Localization (CVPR 2025 · similarity 0.86)
- GPVK-VL: Geometry-Preserving Virtual Keyframes for Visual Localization under Large Viewpoint Changes (CVPR 2025 · similarity 0.82)
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