Problem
Research questions and friction points this paper is trying to address.
Ground-to-Satellite Localization
Unconstrained Image Collections
3D Scene Reconstruction
Ground-to-Satellite Localization in Unconstrained Image Collections for 3D Scene Reconstruction
🤖 AI Summary
本文提出一种鲁棒的层次化跨视角定位框架，利用无约束地面图像集合中的几何约束，解决了不受限制图像集三维场景重建中地面到卫星图像定位问题。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Hierarchical Cross-View Localization
Structure-from-Motion (SfM)
Kernel Density Estimation
Unconstrained Image Collections
🔎 Similar Papers
No similar papers found.
