Wrivinder: Towards Spatial Intelligence for Geo-locating Ground Images onto Satellite Imagery
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911450816053248 |
|---|---|
| author | Gudavalli, Chandrakanth Mohammed, Tajuddin Manhar Yadav, Abhay Bhaskar, Ananth Vishnu Prajapati, Hardik Peng, Cheng Chellappa, Rama Chandrasekaran, Shivkumar Manjunath, B. S. |
| author_facet | Gudavalli, Chandrakanth Mohammed, Tajuddin Manhar Yadav, Abhay Bhaskar, Ananth Vishnu Prajapati, Hardik Peng, Cheng Chellappa, Rama Chandrasekaran, Shivkumar Manjunath, B. S. |
| contents | Aligning ground-level imagery with geo-registered satellite maps is crucial for mapping, navigation, and situational awareness, yet remains challenging under large viewpoint gaps or when GPS is unreliable. We introduce Wrivinder, a zero-shot, geometry-driven framework that aggregates multiple ground photographs to reconstruct a consistent 3D scene and align it with overhead satellite imagery. Wrivinder combines SfM reconstruction, 3D Gaussian Splatting, semantic grounding, and monocular depth--based metric cues to produce a stable zenith-view rendering that can be directly matched to satellite context for metrically accurate camera geo-localization. To support systematic evaluation of this task, which lacks suitable benchmarks, we also release MC-Sat, a curated dataset linking multi-view ground imagery with geo-registered satellite tiles across diverse outdoor environments. Together, Wrivinder and MC-Sat provide a first comprehensive baseline and testbed for studying geometry-centered cross-view alignment without paired supervision. In zero-shot experiments, Wrivinder achieves sub-30\,m geolocation accuracy across both dense and large-area scenes, highlighting the promise of geometry-based aggregation for robust ground-to-satellite localization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_14929 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Wrivinder: Towards Spatial Intelligence for Geo-locating Ground Images onto Satellite Imagery Gudavalli, Chandrakanth Mohammed, Tajuddin Manhar Yadav, Abhay Bhaskar, Ananth Vishnu Prajapati, Hardik Peng, Cheng Chellappa, Rama Chandrasekaran, Shivkumar Manjunath, B. S. Computer Vision and Pattern Recognition Aligning ground-level imagery with geo-registered satellite maps is crucial for mapping, navigation, and situational awareness, yet remains challenging under large viewpoint gaps or when GPS is unreliable. We introduce Wrivinder, a zero-shot, geometry-driven framework that aggregates multiple ground photographs to reconstruct a consistent 3D scene and align it with overhead satellite imagery. Wrivinder combines SfM reconstruction, 3D Gaussian Splatting, semantic grounding, and monocular depth--based metric cues to produce a stable zenith-view rendering that can be directly matched to satellite context for metrically accurate camera geo-localization. To support systematic evaluation of this task, which lacks suitable benchmarks, we also release MC-Sat, a curated dataset linking multi-view ground imagery with geo-registered satellite tiles across diverse outdoor environments. Together, Wrivinder and MC-Sat provide a first comprehensive baseline and testbed for studying geometry-centered cross-view alignment without paired supervision. In zero-shot experiments, Wrivinder achieves sub-30\,m geolocation accuracy across both dense and large-area scenes, highlighting the promise of geometry-based aggregation for robust ground-to-satellite localization. |
| title | Wrivinder: Towards Spatial Intelligence for Geo-locating Ground Images onto Satellite Imagery |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2602.14929 |