Visual Reasoning for Ground to Aerial Localization
Published:Dec 30, 2025 18:36
•1 min read
•ArXiv
Analysis
This paper introduces ViReLoc, a novel framework for ground-to-aerial localization using only visual representations. It addresses the limitations of text-based reasoning in spatial tasks by learning spatial dependencies and geometric relations directly from visual data. The use of reinforcement learning and contrastive learning for cross-view alignment is a key aspect. The work's significance lies in its potential for secure navigation solutions without relying on GPS data.
Key Takeaways
- •Proposes ViReLoc, a visual reasoning framework for ground-to-aerial localization.
- •Utilizes visual representations for planning and localization, avoiding reliance on text-based reasoning.
- •Employs reinforcement learning and contrastive learning for improved spatial reasoning and cross-view alignment.
- •Demonstrates potential for secure navigation without GPS.
Reference
“ViReLoc plans routes between two given ground images.”