用地面图像与卫星图匹配，实现无约束场景的精准三维重建定位。
Ground-to-Satellite Localization in Unconstrained Image Collections for 3D Scene Reconstruction
- 基于SfM模型生成粗到精的跨视角姿态猜测，融合几何约束提升鲁棒性。
- 在挑战性图像集上实现可靠定位，支持真实尺度估计和多重建模型合并。
- 适合野外三维建模、地理信息整合，尤其适用于无初始位置的场景。
从无约束地面图像集合中实现与卫星影像的精准定位，是构建度量准确、地理标注的3D场景模型的关键。现有跨视图定位方法通常依赖全景图像或已知初始位置，限制了其在真实复杂环境中的应用。本文提出一种鲁棒的分层跨视图定位框架，利用从无约束地面图像中提取的SfM模型提供的几何约束。通过跨视图匹配生成粗到精的姿态假设，并采用核密度估计聚合多个SfM模型的噪声预测，恢复共识对齐并剔除异常值。实验表明该方法在困难图像集合上表现稳定。实证发现，基于卫星的对齐可实现精确度量尺度估计、孪生体检测以及分离的SfM重建模型的合并，从而生成比仅使用SfM更完整、地理标注更准确的场景模型。
原文摘要 · Abstract (English)
Ground image localization with respect to satellite imagery is a key enabler for metrically-accurate, geo-localized 3D scene reconstruction from unconstrained image collections. Existing cross-view localization methods have strict requirements such as panoramic imagery or known initial locations, limiting their applicability for in-the-wild reconstruction settings. We propose a robust hierarchical cross-view localization framework that leverages geometric constraints from Structure-from-Motion (SfM) models derived from unconstrained ground image collections. Our method generates coarse-to-fine pose hypotheses through a cross-view matching approach and aggregates noisy predictions across SfM model(s) using Kernel Density Estimation to recover consensus alignments while filtering outliers. Experiments demonstrate reliable localization performance from challenging image collections. Empirically we found satellite-referenced alignment enables accurate metric scale estimation, doppelgänger detection, and merging of disjoint SfM reconstructions, resulting in more complete, geo-localized site models than are possible with SfM alone.
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