AsymLoc: Towards Asymmetric Feature Matching for Efficient Visual Localization
Precise and real-time visual localization is critical for applications like AR/VR and robotics, especially on resource-constrained edge devices such as smart glasses, where battery life and heat dissipation can be a primary concerns. While many efficient models exist, further reducing compute without sacrificing accuracy is essential for practical deployment. To address this, we propose asymmetric visual localization: a large Teacher model processes pre-mapped database images offline, while a lightweight Student model processes the query image online. This creates a challenge in matching features from two different models without resorting to heavy, learned matchers. We introduce AsymLoc,...
| Authors | Mohammad Omama, Gabriele Berton, Eric Foxlin et al. |
|---|---|
| Primary category | cs.CV |
| Published | 10 April 2026 |
| Last updated at source | 2 July 2026 |
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