GeoFlow: Real-Time Fine-Grained Cross-View Geolocalization via Iterative Flow Prediction

TL;DR

GeoFlow is a lightweight, real-time geolocalization method that uses iterative refinement to accurately estimate a ground image's location relative to satellite imagery, balancing speed and precision.

Contribution

The paper introduces GeoFlow, a novel probabilistic framework with an iterative inference algorithm that achieves real-time, high-accuracy cross-view geolocalization without re-training.

Findings

Runs at 29 FPS on KITTI and VIGOR datasets

Achieves state-of-the-art efficiency with competitive accuracy

Enables flexible inference-time performance scaling

Abstract

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.