MambaGlue: Fast and Robust Local Feature Matching With Mamba

• URL: http://arxiv.org/abs/2502.00462v1
• Date: Sat, 01 Feb 2025 15:43:03 GMT
• Title: MambaGlue: Fast and Robust Local Feature Matching With Mamba
• Authors: Kihwan Ryoo, Hyungtae Lim, Hyun Myung,
• Abstract summary: We propose a novel Mamba-based local feature matching approach, called MambaGlue.<n>Mamba is an emerging state-of-the-art architecture rapidly gaining recognition for its superior speed in both training and inference.<n>Our MambaGlue achieves a balance between robustness and efficiency in real-world applications.
• Score: 9.397265252815115
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In recent years, robust matching methods using deep learning-based approaches have been actively studied and improved in computer vision tasks. However, there remains a persistent demand for both robust and fast matching techniques. To address this, we propose a novel Mamba-based local feature matching approach, called MambaGlue, where Mamba is an emerging state-of-the-art architecture rapidly gaining recognition for its superior speed in both training and inference, and promising performance compared with Transformer architectures. In particular, we propose two modules: a) MambaAttention mixer to simultaneously and selectively understand the local and global context through the Mamba-based self-attention structure and b) deep confidence score regressor, which is a multi-layer perceptron (MLP)-based architecture that evaluates a score indicating how confidently matching predictions correspond to the ground-truth correspondences. Consequently, our MambaGlue achieves a balance between robustness and efficiency in real-world applications. As verified on various public datasets, we demonstrate that our MambaGlue yields a substantial performance improvement over baseline approaches while maintaining fast inference speed. Our code will be available on https://github.com/url-kaist/MambaGlue

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