SAMatcher
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...
Fonte do modelo
- Tipo
- Other
- Tamanho do arquivo
- 915 MB
- Modelo base
- syscv-community/sam-hq-vit-large
- Versão
- vRevision 03ccaf1
- Atualizado
- 1 de nov. de 2026
- Downloads
- 0
- Fonte
- Hugging Face
Descrição da fonte
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}
}
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Trechos de fonte
2 trechos--- 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} } ```
Source context: 0 downloads · 0 likes · Pipeline image-segmentation · Repo SSSSphinx/SAMatcher
