Lifting Vision: Ground to Aerial Localization with Reasoning Guided Planning
Soham Pahari, M. Srinivas

TL;DR
This paper introduces ViReLoc, a visual reasoning framework for localization that learns spatial relations from visual data alone, improving navigation accuracy without relying on GPS.
Contribution
The paper proposes a novel visual reasoning paradigm, ViReLoc, which enables route planning and localization solely from visual representations, bypassing the need for textual or GPS data.
Findings
ViReLoc improves spatial reasoning accuracy in navigation tasks.
The framework enhances cross view retrieval performance.
Experiments demonstrate robustness across diverse scenarios.
Abstract
Multimodal intelligence development recently show strong progress in visual understanding and high level reasoning. Though, most reasoning system still reply on textual information as the main medium for inference. This limit their effectiveness in spatial tasks such as visual navigation and geo-localization. This work discuss about the potential scope of this field and eventually propose an idea visual reasoning paradigm Geo-Consistent Visual Planning, our introduced framework called Visual Reasoning for Localization, or ViReLoc, which performs planning and localization using only visual representations. The proposed framework learns spatial dependencies and geometric relations that text based reasoning often suffer to understand. By encoding step by step inference in the visual domain and optimizing with reinforcement based objectives, ViReLoc plans routes between two given ground…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMultimodal Machine Learning Applications · Robotics and Sensor-Based Localization · Constraint Satisfaction and Optimization
