arXiv Computer Vision
MatchCityLoc: Aerial Localization in LoD City Models via Surface Normal Matching
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Jul 22
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.
arXiv Computer Vision
Sep 17
RIM: A Retrieval-In-Matching Framework for Cross-Domain Global Visual Localization of UAVs
arXiv:2607.20116v2 Announce Type: replace Abstract: Global visual localization of unmanned aerial vehicles (UAVs) using remote-sensing reference maps has attracted increasing attention. However, diff...
arXiv AI
Jun 30
Beyond 2D Matching: A Unified Single-Stage Framework for Geometry-Aware Cross-View Object Geo-Localization
arXiv:2606. 30576v1 Announce Type: cross Abstract: Cross-view object geo-localization (CVOGL) aims to locate a target object from a query view (e.
arXiv Computer Vision
Aug 25
DECO: Depth-Guided Co-Visibility Reasoning for Low-Altitude UAV Visual Localization
arXiv:2608.22289v1 Announce Type: new Abstract: Unmanned aerial vehicles (UAVs) increasingly require robust visual localization in GNSS-denied environments. A common solution estimates UAV poses by m...
arXiv Computer Vision
Sep 1
Pixel-wise Geo-registration of Drone and Satellite Images
arXiv:2608.28891v1 Announce Type: new Abstract: Pixel-level cross-view geo-registration aims to align a query image (e.g., drone) to a geo-referenced satellite map so that every query pixel can be ma...
arXiv Computer Vision
Sep 7
ARC-Loc: Leveraging Azimuthal Ray Convergence as a Geometric Cue for Direct Cross-View Localization
ARC‑Loc introduces a new cross‑view localization method that bypasses heavy Bird’s‑Eye‑View transformations and external depth models. By converting ground keypoints into azimuthal rays on a satellite map and exploiting their convergence at the user’s location, the approach uses a minimal Azimuthal Ray Convergence solver and an ARC loss to directly match ground and satellite images. Experiments on VIGOR and KITTI show that ARC‑Loc achieves competitive accuracy while offering faster, memory‑efficient inference and easy integration with existing frameworks.