--- base_model: - syscv-community/sam-hq-vit-large language: - en license: apache-2.0 pipeline_tag: image-segmentation tags: - feature-matching - computer-vision - co-visibility --- # 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](https://huggingface.co/papers/2606.03406) - **Authors:** Xu Pan, Qiyuan Ma, Jintao Zhang, He Chen, Xianwei Zheng - **GitHub:** [https://github.com/TwSphinx54/SAMatcher](https://github.com/TwSphinx54/SAMatcher) - **Project Page:** [https://xupan.top/Projects/samatcher](https://xupan.top/Projects/samatcher) ## Quick Start ### Installation The project uses `uv` for dependency management. Recommended Python version is 3.12. ```bash # 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: ```bash 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 bash scripts/evaluate_megadepth.sh ``` ## Citation If you find SAMatcher useful in your research, please consider citing: ```bibtex @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} } ```