GeoFlow实现实时高精度跨视角定位，速度达29帧/秒。
GeoFlow: Real-Time Fine-Grained Cross-View Geolocalization via Iterative Flow Prediction
- 直接预测位置偏差，构建概率映射模型。
- 迭代采样算法使定位精度达90%以上，速度29 FPS。
- 无需重训练即可灵活调节计算与精度平衡，适合车载系统。
精准快速的定位对无GPS环境下的自动驾驶安全至关重要。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。然而现有方法在精度与速度间存在难以调和的权衡，高精度模型通常无法实时运行。本文提出GeoFlow，一种轻量高效的新框架，打破这一困局。该方法学习直接的概率映射，预测任意初始位置假设所需的位移（距离与方向）以进行修正。配合创新的推理算法——迭代精炼采样（IRS），不依赖单一预测，而是让一组假设从随机起点逐步‘流动’至稳定共识，实现鲁棒收敛。尽管具有迭代特性，该方法仍支持推理时灵活调整计算量与性能，无需重新训练。在KITTI与VIGOR数据集上的实验表明，GeoFlow达到业界领先效率，实现实时运行速度29 FPS，同时保持竞争力的定位精度。本工作为实用化实时地理定位系统开辟了新路径。
原文摘要 · Abstract (English)
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.
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