VMatcher: State-Space Semi-Dense Local Feature Matching

• URL: http://arxiv.org/abs/2507.23371v1
• Date: Thu, 31 Jul 2025 09:39:16 GMT
• Title: VMatcher: State-Space Semi-Dense Local Feature Matching
• Authors: Ali Youssef,
• Abstract summary: VMatcher is a hybrid Mamba-Transformer network for semi-dense feature matching between image pairs.<n>VMatcher integrates Mamba's highly efficient long-sequence processing with the Transformer's attention mechanism.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper introduces VMatcher, a hybrid Mamba-Transformer network for semi-dense feature matching between image pairs. Learning-based feature matching methods, whether detector-based or detector-free, achieve state-of-the-art performance but depend heavily on the Transformer's attention mechanism, which, while effective, incurs high computational costs due to its quadratic complexity. In contrast, Mamba introduces a Selective State-Space Model (SSM) that achieves comparable or superior performance with linear complexity, offering significant efficiency gains. VMatcher leverages a hybrid approach, integrating Mamba's highly efficient long-sequence processing with the Transformer's attention mechanism. Multiple VMatcher configurations are proposed, including hierarchical architectures, demonstrating their effectiveness in setting new benchmarks efficiently while ensuring robustness and practicality for real-time applications where rapid inference is crucial. Source Code is available at: https://github.com/ayoussf/VMatcher

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