[2605.05351] egenioussBench: A New Dataset for Geospatial Visual Localisation
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Computer Science > Computer Vision and Pattern Recognition
arXiv:2605.05351 (cs)
[Submitted on 6 May 2026]
Title:egenioussBench: A New Dataset for Geospatial Visual Localisation
Authors:Phillipp Fanta-Jende, Francesco Vultaggio, Alexander Kern, Yasmin Loeper, Markus Gerke
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Abstract:We present egenioussBench, a visual localisation benchmark built on geospatial reference data: a city-scale airborne 3D mesh and a CityGML LoD2 model. This pairing reflects deployable mapping assets and supports true scalability beyond traditional SfM-based approaches. The query data comprise smartphone images with centimetre-accurate, map-independent ground truth obtained via PPK and GCP/CP-aided adjustment. From 2,709 images, we derive a non-co-visible subset by estimating the full co-visibility matrix from rendered depth and selecting a maximum independent set; the released data include a test split of 42 non-co-visible images with withheld ground truth and a validation split of 412 sequential images with poses, e.g. for training of pose regressors and self-validation. The benchmark features a public leaderboard evaluated with binning metrics at multiple pose-error thresholds alongside global statistics (median, RMSE, outlier ratio), ensuring fair, like-for-like comparison across mesh- and LoD2-based methods. Together, these design choices expose realistic cross-view and cross-domain challenges while providing a rigorous, scalable path for advancing large-scale visual localisation. We make the evaluation code and data availeable at this https URL and this https URL
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2605.05351 [cs.CV]
(or arXiv:2605.05351v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2605.05351
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arXiv-issued DOI via DataCite
Related DOI:
https://doi.org/10.5194/isprs-archives-XLVIII-1-W6-2025-75-2025
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Submission history
From: Francesco Vultaggio [view email] [v1] Wed, 6 May 2026 18:23:39 UTC (7,997 KB)
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