RIM: A Retrieval-In-Matching Framework for Cross-Domain Global Visual Localization of UAVs
Global visual localization of unmanned aerial vehicles (UAVs) using remote-sensing reference maps has attracted increasing attention. However, acquisition-time and imaging-platform differences between UAV and reference imagery induce substantial cross-domain appearance and viewpoint shifts, challenging robust six-degree-of-freedom (6-DoF) pose estimation. We address these shifts by sampling UAV-viewpoint reference views from Google 3D Tiles across locations, altitudes, and orientations. A two-stage cross-domain fine-tuning recipe adapts SALAD using pose-near positives and geographically distan
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- PossiblePossibly related (embedding) · 45%Showcase: geolocating a dashcam video without GPS, only from the footage [P] →
- FuzzyOverlapping authors or contributors · 62%bytedance/deer-flow →
“Shared author/contributor keys: wang”
- FuzzyOverlapping authors or contributors · 62%ray-project/ray →
“Shared author/contributor keys: wang”
- LinkedLinked via arxiv author · 85%Wangxin Liu →
“RIM: A Retrieval-In-Matching Framework for Cross-Domain Global Visual Localization of UAVs”
- LinkedLinked via arxiv author · 85%Siyuan Duan →
“RIM: A Retrieval-In-Matching Framework for Cross-Domain Global Visual Localization of UAVs”
- LinkedLinked via arxiv author · 85%Shangshang Wang →
“RIM: A Retrieval-In-Matching Framework for Cross-Domain Global Visual Localization of UAVs”
- LinkedLinked via arxiv author · 85%Zhimin Mao →
“RIM: A Retrieval-In-Matching Framework for Cross-Domain Global Visual Localization of UAVs”
- LinkedLinked via arxiv author · 85%Bingliang Hu →
“RIM: A Retrieval-In-Matching Framework for Cross-Domain Global Visual Localization of UAVs”