SigMa: Semantic Similarity-Guided Semi-Dense Feature Matching.

Abstract

Recent advancements have led the image matching community to increasingly focus on obtaining subpixel-level correspondences in a detector-free manner, i.e., semi-dense feature matching. Existing methods tend to overfocus on low-level local features while ignoring equally important high-level semantic information. To tackle these shortcomings, we propose SigMa, a semantic similarity-guided semi-dense feature matching method, which leverages the strengths of both local features and high-level semantic features. First, we design a dual-branch feature extractor, comprising a convolutional network and a vision foundation model, to extract low-level local features and high-level semantic features, respectively. To fully retain the advantages of these two features and effectively integrate them, we also introduce a cross-domain feature adapter, which could overcome their spatial resolution mismatches, channel dimensionality variations, and inter-domain gaps. Furthermore, we observe that performing the transformer on the whole feature map is unnecessary because of the similarity of local representations. We design a guided pooling method based on semantic similarity. This strategy performs attention computation by selecting highly semantically similar regions, aiming to minimize information loss while maintaining computational efficiency. Extensive experiments on multiple datasets demonstrate that our method achieves a competitive accuracy-efficiency trade-off across various tasks and exhibits strong generalization capabilities across different datasets. Additionally, we conduct a series of ablation studies and analysis experiments to validate the effectiveness and rationality of our method's design. Our code is publicly available at https://github.com/ShineFox/SigMa.