Abstract
Image matching is a fundamental problem in computer vision, serving as a core component in tasks such as visual localization, structure from motion, and SLAM. While recent advances using convolutional neural networks and transformer have achieved impressive accuracy, their substantial computational demands hinder practical deployment on resource-constrained devices, such as mobile and embedded platforms. To address this challenge, we propose LIM, a lightweight image local feature matching network designed for computationally constrained embedded systems. LIM integrates efficient feature extraction and matching modules that significantly reduce model complexity while maintaining competitive performance. Our design emphasizes robustness to extreme viewpoint and rotational variations, making it suitable for real-world deployment scenarios. Extensive experiments on multiple benchmarks demonstrate that LIM achieves a favorable trade-off between speed and accuracy, running more than 3× faster than existing deep matching methods, while preserving high-quality matching results. These characteristics position LIM as an effective solution for real-time applications in power-limited environments.