GeoFlow：基于迭代流预测的实时细粒度跨视角地理定位
GeoFlow: Real-Time Fine-Grained Cross-View Geolocalization via Iterative Flow Prediction
提出一种基于迭代流预测的轻量级跨视角定位框架，其推理算法 IRS 通过迭代采样实现灵活的精度-速度权衡，可复用于其他需要实时精确定位的视觉任务。
原文依据 GeoFlow achieves state-of-the-art efficiency, running at real-time speeds of 29 FPS while maintaining competitive localization accuracy.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论准确且快速的定位对于GPS拒止区域的安全自主导航至关重要。细粒度跨视角地理定位（FG-CVG）旨在估计地面图像相对于卫星图像的精确二维自由度（2-DoF）位置。然而，当前方法被迫在精度与速度之间艰难权衡，高精度模型往往速度慢，难以实时应用。本文提出GeoFlow，一种轻量且高效的框架，打破了精度与速度的权衡。该技术学习一种直接的概率映射，预测纠正任意给定位置假设所需的位移（距离和方向）。并辅以我们新颖的推理算法——迭代细化采样（IRS）。IRS不依赖单一预测，而是细化一组假设，使它们从随机起点迭代地“流动”到稳健的收敛共识。尽管具有迭代特性，该方法提供了灵活的推理时缩放，允许在无需重新训练的情况下直接权衡性能与计算量。在KITTI和VIGOR数据集上的实验表明，GeoFlow实现了最优效率，以29 FPS的实时速度运行，同时保持有竞争力的定位精度。这项工作为开发实用的实时地理定位系统开辟了新路径。
英文原文摘要 1404 字符
Accurate and fast localization is vital for safe autonomous navigation in GPS-denied areas. Fine-Grained Cross-View Geolocalization (FG-CVG) aims to estimate the precise 2-Degree-of-Freedom (2-DoF) location of a ground image relative to a satellite image. However, current methods force a difficult trade-off, with high-accuracy models being slow for real-time use. In this paper, we introduce GeoFlow, a new approach that offers a lightweight and highly efficient framework that breaks this accuracy-speed trade-off. Our technique learns a direct probabilistic mapping, predicting the displacement (in distance and direction) required to correct any given location hypothesis. This is complemented by our novel inference algorithm, Iterative Refinement Sampling (IRS). Instead of trusting a single prediction, IRS refines a population of hypotheses, allowing them to iteratively 'flow' from random starting points to a robust, converged consensus. Even its iterative nature, this approach offers flexible inference-time scaling, allowing a direct trade-off between performance and computation without any re-training. Experiments on the KITTI and VIGOR datasets show that GeoFlow achieves state-of-the-art efficiency, running at real-time speeds of 29 FPS while maintaining competitive localization accuracy. This work opens a new path for the development of practical real-time geolocalization systems.