Papers › LandscapeAR: Large Scale Outdoor Augmented Reality by Matching Photographs with...
LandscapeAR: Large Scale Outdoor Augmented Reality by Matching Photographs with Terrain Models Using Learned Descriptors
Jan Brejcha, Michal Lukáč, Yannick Hold-Geoffroy, Oliver Wang, Martin Čadík
We introduce a solution to large scale Augmented Reality for outdoor scenes by registering camera images to textured Digital Elevation Models (DEMs). To accomodate the inherent differences in appearance between real images and DEMs, we train a cross-domain feature descriptor using Structure From Motion (SFM) reconstructions to acquire training data. Our method runs efficiently on a mobile device, and outperforms existing learned and hand designed feature descriptors for this task.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Patch Matching | HPatches | LSAR-aux-render | Patch Matching | 45.3 | #2 of 2 | Archive leaderboard | report |
| Patch Matching | HPatches | LSAR-aux-render | Patch Retrieval | 55.6 | #2 of 2 | Archive leaderboard | report |
| Patch Matching | HPatches | LSAR-aux-render | Patch Verification | 95.6 | #2 of 2 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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