egenioussBench: A New Dataset for Geospatial Visual Localisation

• URL: http://arxiv.org/abs/2605.05351v1
• Date: Wed, 06 May 2026 18:23:39 GMT
• Title: egenioussBench: A New Dataset for Geospatial Visual Localisation
• Abstract summary: We present egenioussBench, a visual localisation benchmark built on geospatial reference data.<n>This pairing reflects deployable mapping assets and supports true scalability beyond traditional SfM-based approaches.<n>The benchmark features a public leaderboard evaluated with binning metrics at multiple pose-error thresholds.
• Score: 36.94429692322632
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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 https://github.com/fratopa/egenioussBench and https://www.egeniouss.eu/

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