SAMatcher: Co-Visibility Modeling with Segment Anything for Robust Feature Matching

SAMatcher is a feature matching framework that formulates correspondence estimation through co-visibility modeling. Built upon the Segment Anything Model (SAM), it introduces a symmetric cross-view interaction mechanism that enables bidirectional feature exchange and cross-view semantic alignment to improve correspondence under large viewpoint, illumination, and scale variations.

• Paper: SAMatcher: Co-Visibility Modeling with Segment Anything for Robust Feature Matching
• Authors: Xu Pan, Qiyuan Ma, Jintao Zhang, He Chen, Xianwei Zheng
• GitHub: https://github.com/TwSphinx54/SAMatcher
• Project Page: https://xupan.top/Projects/samatcher

Quick Start

Installation

The project uses uv for dependency management. Recommended Python version is 3.12.

# Clone the repository
git clone https://github.com/TwSphinx54/SAMatcher.git
cd SAMatcher

# Install dependencies using uv
uv sync --frozen

Download Weights

You can download the model weights using the Hugging Face CLI:

huggingface-cli download SSSSphinx/SAMatcher --local-dir ./weights/SAMatcher

Then follow the official training/evaluation scripts in the GitHub repository.

Evaluation

To evaluate on MegaDepth, use the provided script:

bash scripts/evaluate_megadepth.sh

Citation

If you find SAMatcher useful in your research, please consider citing:

@article{pan2024samatcher,
  title={SAMatcher: Co-Visibility Modeling with Segment Anything for Robust Feature Matching},
  author={Pan, Xu and Ma, Qiyuan and Zhang, Jintao and Chen, He and Zheng, Xianwei},
  journal={arXiv preprint arXiv:2606.03406},
  year={2024}
}