Abstract
Drone-view geo-localization (DVGL) aims to determine the location of drones in GPS-denied environments by retrieving the corresponding geotagged satellite tile from a reference gallery given UAV observations of a location. In many existing formulations, these observations are represented by a single oblique UAV image. In contrast, our satellite-free setting is designed for multi-view UAV sequences, which are used to construct a geometry-normalized UAV-side location representation before cross-view retrieval. Existing approaches rely on satellite imagery during training, either through paired supervision or unsupervised alignment, which limits practical deployment when satellite data are unavailable or restricted. In this paper, we propose a satellite-free training (SFT) framework that converts drone imagery into cross-view compatible representations through three main stages: drone-side 3D scene reconstruction, geometry-based pseudo-orthophoto generation, and satellite-free feature aggregation for retrieval. Specifically, we first reconstruct dense 3D scenes from multi-view drone images using 3D Gaussian splatting and project the reconstructed geometry into pseudo-orthophotos via PCA-guided orthographic projection. This rendering stage operates directly on reconstructed scene geometry without requiring camera parameters at rendering time. Next, we refine these orthophotos with lightweight geometry-guided inpainting to obtain texture-complete drone-side views. Finally, we extract DINOv3 patch features from the generated orthophotos, learn a Fisher vector aggregation model solely from drone data, and reuse it at test time to encode satellite tiles for cross-view retrieval. Experimental results on University-1652 and SUES-200 show that our SFT framework substantially outperforms satellite-free generalization baselines and narrows the gap to methods trained with satellite imagery.
Abstract (translated)
无人机视角地理定位（DVGL）旨在通过从参考图库中检索对应的地理标记卫星瓦片，来确定无人机在GPS拒止环境中的位置，其输入是无人机对某地点的观测。在许多现有方案中，这些观测由单张倾斜无人机图像表示。相比之下，本文设计的无卫星场景适用于多视角无人机序列，用于在跨视角检索前构建几何归一化的无人机端位置表示。现有方法在训练阶段依赖卫星影像，无论是通过配对监督还是无监督对齐，这限制了卫星数据不可用或受限时的实际部署。本文提出一种无卫星训练（SFT）框架，通过三个主要阶段将无人机影像转换为跨视角兼容表示：无人机端三维场景重建、基于几何的伪正射影像生成，以及用于检索的无卫星特征聚合。具体而言，首先使用三维高斯泼溅（3D Gaussian splatting）从多视角无人机图像重建稠密三维场景，并通过PCA引导的正射投影将重建几何投影为伪正射影像。该渲染阶段直接基于重建的场景几何运行，无需渲染时的相机参数。随后，使用轻量级几何引导修复技术细化这些正射影像，以获得纹理完整的无人机端视图。最后，从生成的正射影像中提取DINOv3块特征，仅从无人机数据学习Fisher向量聚合模型，并在测试时复用该模型对卫星瓦片进行编码以实现跨视角检索。在University-1652和SUES-200数据集上的实验结果表明，所提SFT框架显著优于无卫星泛化基线，并缩小了与使用卫星影像训练的方法之间的性能差距。
URL
https://arxiv.org/abs/2604.01581
