arXiv Computer Vision
Ground-to-Satellite Localization in Unconstrained Image Collections for 3D Scene Reconstruction
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
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
3d ago
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
Aug 25
GeoLink: A 3D-aware Framework to Improve Generalization for Cross-view Geo-localization
arXiv:2604.13183v4 Announce Type: replace Abstract: Generalizable cross-view geo-localization aims to match the same location across views in unseen regions and conditions without GPS supervision. It...
Hugging Face Trending Papers
Aug 10
Warp-free Cross-view Geo-localization via Feature-space Consensus Mining
Cross-view geo-localization is challenging due to drastic viewpoint changes and large appearance discrepancies between street-level and satellite imagery. Although existing methods often use geometric warping to expose co-visible cues, such transformations rely on restrictive spatial assumptions and inevitably introduce severe visual distortions under view-dependent visibility, yielding noisy supervision and fragile correspondences.
arXiv Computer Vision
Aug 27
OpenCVL: An Open, Diverse, and Large-Scale Dataset for Fine-Grained Cross-View Localization
OpenCVL is a large, open dataset for fine-grained cross-view localization, comprising 617,388 ground‑aerial image pairs from 41 European cities. It blends high‑end sensor data with diverse in‑the‑wild images and includes a curation framework to correct pose annotations, enabling reliable evaluation. The dataset also offers cross‑area and snowy test sets to probe generalization, and experiments show that adding noisy in‑the‑wild data improves model performance on clean tests.
Hugging Face Trending Papers
Jun 29
Argus: Metric Panoramic 3D Reconstruction for Indoor Scenes
Metric feed-forward 3D reconstruction for panoramic data remains under-explored due to the lack of large-scale panoramic RGB-D training data. We present Realsee3D, a hybrid dataset of 10K indoor scenes (1K real, 9K synthetic) with 299K panoramic viewpoints and precise metric annotations, and Argus, a feed-forward network trained on it for metric panoramic 3D reconstruction.