构建城市级地理视觉定位基准，支持高精度真实场景测试。
egenioussBench: A New Dataset for Geospatial Visual Localisation
- 基于空载3D网格与CityGML LoD2模型，实现可部署的地理参考数据。
- 42张非共可见图像+412张连续图像，含厘米级独立地面真值。
- 公开排行榜支持多阈值评估，适合大规模视觉定位算法对比。
我们提出egenioussBench，一个基于地理空间参考数据的视觉定位基准：包含城市尺度的航空3D网格和CityGML LoD2模型。该组合反映实际部署的地图资产，支持超越传统SfM方法的可扩展性。查询数据由智能手机图像构成，通过PPK与GCP/CP辅助校正获得厘米级、地图无关的地面真值。从2,709张图像中，通过渲染深度估计完整共可见矩阵，并选择最大独立集得到非共可见子集；发布数据包含42张非共可见测试图像（地面真值隐藏）和412张连续验证图像（含位姿），适用于位姿回归器训练与自验证。基准提供公开排行榜，采用分箱评估指标在多个位姿误差阈值下进行评估，并包含全局统计量（中位数、RMSE、异常值比例），确保基于网格与LoD2方法之间的公平、同质比较。这些设计选择揭示了真实的跨视图与跨域挑战，同时为大规模视觉定位研究提供了严谨、可扩展的推进路径。评估代码与数据可在https://github.com/fratopa/egenioussBench 和 https://www.egeniouss.eu/ 获取。
原文摘要 · Abstract (English)
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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