Investigating Visual Localization Using Geospatial Meshes
This paper investigates the use of geospatial mesh data for visual localization, focusing on city-scale aerial meshes as map representations for locating ground-level query images captured by smartphones. Visual localization, essential for applications such as robotics and augmented reality, traditi...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2024-12-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://isprs-archives.copernicus.org/articles/XLVIII-2-W8-2024/447/2024/isprs-archives-XLVIII-2-W8-2024-447-2024.pdf |
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| _version_ | 1846122165441658880 |
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| author | F. Vultaggio F. Vultaggio P. Fanta-Jende M. Schörghuber A. Kern M. Gerke |
| author_facet | F. Vultaggio F. Vultaggio P. Fanta-Jende M. Schörghuber A. Kern M. Gerke |
| author_sort | F. Vultaggio |
| collection | DOAJ |
| description | This paper investigates the use of geospatial mesh data for visual localization, focusing on city-scale aerial meshes as map representations for locating ground-level query images captured by smartphones. Visual localization, essential for applications such as robotics and augmented reality, traditionally relies on Structure-from-Motion (SfM) reconstructions or image collections as maps. However, mesh-based approaches offer dense spatial representation, memory efficiency, and real-time rendering capabilities. In this work, we evaluate initialization strategies, image matching techniques, and pose refinement methods for mesh-based localization pipelines, comparing the performance of both traditional and deep-learning-based techniques in image matching between real and synthetic views. We created a dataset from nadir and oblique aerial imagery and accurately georeferenced smartphone images to test cross-modal localization. Our findings demonstrate that combining global feature retrieval with GNSS-based spatial filtering yields significant improvements in accuracy and efficiency, achieving submeter positional and subdegree rotational errors. This study advances scalable visual localization using meshes and highlights the potential of integrating smartphone GNSS data for improved performance in urban environments. |
| format | Article |
| id | doaj-art-2c70677d24c140789eb8a94fbe318688 |
| institution | Kabale University |
| issn | 1682-1750 2194-9034 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj-art-2c70677d24c140789eb8a94fbe3186882024-12-15T02:43:15ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342024-12-01XLVIII-2-W8-202444745410.5194/isprs-archives-XLVIII-2-W8-2024-447-2024Investigating Visual Localization Using Geospatial MeshesF. Vultaggio0F. Vultaggio1P. Fanta-Jende2M. Schörghuber3A. Kern4M. Gerke5Austrian Institute of Technology, Center for Vision, Automation and Control, Unit Assistive and Autonomous Systems, AustriaTechnische Universität Braunschweig, Institute of Geodesy and Photogrammetry, GermanyAustrian Institute of Technology, Center for Vision, Automation and Control, Unit Assistive and Autonomous Systems, AustriaAustrian Institute of Technology, Center for Vision, Automation and Control, Unit Assistive and Autonomous Systems, AustriaTechnische Universität Braunschweig, Institute of Flight Guidance, GermanyTechnische Universität Braunschweig, Institute of Geodesy and Photogrammetry, GermanyThis paper investigates the use of geospatial mesh data for visual localization, focusing on city-scale aerial meshes as map representations for locating ground-level query images captured by smartphones. Visual localization, essential for applications such as robotics and augmented reality, traditionally relies on Structure-from-Motion (SfM) reconstructions or image collections as maps. However, mesh-based approaches offer dense spatial representation, memory efficiency, and real-time rendering capabilities. In this work, we evaluate initialization strategies, image matching techniques, and pose refinement methods for mesh-based localization pipelines, comparing the performance of both traditional and deep-learning-based techniques in image matching between real and synthetic views. We created a dataset from nadir and oblique aerial imagery and accurately georeferenced smartphone images to test cross-modal localization. Our findings demonstrate that combining global feature retrieval with GNSS-based spatial filtering yields significant improvements in accuracy and efficiency, achieving submeter positional and subdegree rotational errors. This study advances scalable visual localization using meshes and highlights the potential of integrating smartphone GNSS data for improved performance in urban environments.https://isprs-archives.copernicus.org/articles/XLVIII-2-W8-2024/447/2024/isprs-archives-XLVIII-2-W8-2024-447-2024.pdf |
| spellingShingle | F. Vultaggio F. Vultaggio P. Fanta-Jende M. Schörghuber A. Kern M. Gerke Investigating Visual Localization Using Geospatial Meshes The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | Investigating Visual Localization Using Geospatial Meshes |
| title_full | Investigating Visual Localization Using Geospatial Meshes |
| title_fullStr | Investigating Visual Localization Using Geospatial Meshes |
| title_full_unstemmed | Investigating Visual Localization Using Geospatial Meshes |
| title_short | Investigating Visual Localization Using Geospatial Meshes |
| title_sort | investigating visual localization using geospatial meshes |
| url | https://isprs-archives.copernicus.org/articles/XLVIII-2-W8-2024/447/2024/isprs-archives-XLVIII-2-W8-2024-447-2024.pdf |
| work_keys_str_mv | AT fvultaggio investigatingvisuallocalizationusinggeospatialmeshes AT fvultaggio investigatingvisuallocalizationusinggeospatialmeshes AT pfantajende investigatingvisuallocalizationusinggeospatialmeshes AT mschorghuber investigatingvisuallocalizationusinggeospatialmeshes AT akern investigatingvisuallocalizationusinggeospatialmeshes AT mgerke investigatingvisuallocalizationusinggeospatialmeshes |