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

2026-03-23

AI Summary

This work addresses the challenge of achieving both high accuracy and real-time performance in fine-grained cross-view geolocalization under GPS-denied conditions. To this end, we propose GeoFlow, a framework that employs a lightweight neural network to learn the probability distribution of spatial displacements and incorporates an iterative refinement sampling strategy during inference. This design enables flexible trade-offs between computational cost and localization accuracy without requiring retraining. By effectively decoupling the traditional accuracy–speed trade-off, GeoFlow achieves real-time inference at 29 frames per second while maintaining state-of-the-art localization precision on the KITTI and VIGOR benchmarks.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Computer Vision: Remote Sensing / Geospatial AI
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Fine-Grained Cross-View Geolocalization
• real-time localization
• accuracy-speed trade-off
• GPS-denied navigation
• 2-DoF localization

Innovation

• GeoFlow
• Iterative Refinement Sampling
• Cross-View Geolocalization
• Real-Time Localization
• Probabilistic Displacement Prediction