LIM: A Low-Complexity Local Feature Image Matching Network for Real-Time Embedded Applications

Abstract

Image matching is a fundamental task in computer vision, underpinning applications such as visual localization and structure-from-motion. While deep convolutional neural network (CNN)-based approaches have achieved high detection accuracy, their high computational cost limits their deployment on resource-constrained platforms such as mobile and embedded systems. This paper presents a lightweight image matching network that achieves a favorable trade-off between accuracy and efficiency. The proposed model further enhances robustness to large image rotations, a common challenge in aerial and robotics applications. Extensive experiments demonstrate that our method maintains competitive accuracy while significantly reducing inference time compared to existing CNN-based approaches.