面向3D场景重建的无约束图像集合中地面对卫星定位
Ground-to-Satellite Localization in Unconstrained Image Collections for 3D Scene Reconstruction
- Center for Vision Technologies, SRI International（SRI国际公司视觉技术中心）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究针对无约束图像集合的地面对卫星定位问题，提出结合SfM模型几何约束与核密度估计的分层跨视图定位框架，实现可靠定位，助力更完整的地理定位3D场景重建。
AI中文摘要：
相对于卫星图像的地面图像定位，是从无约束图像集合中生成具有度量精度、地理定位的3D场景重建的关键支撑技术。现有的跨视图定位方法存在严格要求，比如需要全景图像或已知初始位置，这限制了它们在野外重建场景中的适用性。我们提出了一种鲁棒的分层跨视图定位框架，该框架利用从无约束地面图像集合得到的运动恢复结构（SfM）模型的几何约束。我们的方法通过跨视图匹配方法生成从粗到细的位姿假设，并使用核密度估计聚合来自SfM模型的噪声预测，以恢复共识对齐同时过滤异常值。实验表明，该方法在具有挑战性的图像集合中表现出可靠的定位性能。我们通过经验发现，卫星参考对齐能够实现准确的度量尺度估计、替身（doppelgänger）检测以及合并不相交的SfM重建，从而生成比仅使用SfM更完整、地理定位的站点模型。
英文摘要：
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.
