Scalable Feature Matching via State Space Modeling and Sparse Correlation

Efficient and robust feature matching is crucial for latency-sensitive and resource-constrained applications. While current semi-dense feature matching approaches commonly suffer from quadratic complexity in spatial resolution due to transformer-based long-range context modeling or redundant full correlation computations. To overcome these limitations, we present a novel scalable feature matching method that delivers reliable correspondences with low memory footprint and latency, especially at high resolutions. Our approach introduces three key innovations: (1) a hybrid Conv-Mamba backbone for efficient cross-scale and cross-view feature extraction with linear complexity, (2) a training-free norm-based feature filtering mechanism, enabling sparse correlation that significantly reduces computation overhead during inference, and (3) a lightweight recurrent coordinate refinement that surpasses expectation-based regression in subpixel accuracy. Experimental results demonstrate our method's superior accuracy and efficiency performance over state-of-the-art (SOTA) approaches on both indoor and outdoor datasets. Notably, in resolution scaling tests, our method achieves 45% lower memory usage and 2.4$\times$ faster inference than JamMa, while also outperforming Efficient LoFTR with 57% memory reduction and 1.8$\times$ speedup at high resolution. These results demonstrate the strong scalability and practical efficiency of our method.