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--- |
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tags: |
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- image-to-3d |
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- pytorch_model_hub_mixin |
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- model_hub_mixin |
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library_name: mast3r |
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repo_url: https://github.com/naver/mast3r |
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--- |
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## Grounding Image Matching in 3D with MASt3R |
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```bibtex |
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@misc{mast3r_arxiv24, |
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title={Grounding Image Matching in 3D with MASt3R}, |
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author={Vincent Leroy and Yohann Cabon and Jerome Revaud}, |
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year={2024}, |
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eprint={2406.09756}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV} |
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} |
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@inproceedings{dust3r_cvpr24, |
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title={DUSt3R: Geometric 3D Vision Made Easy}, |
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author={Shuzhe Wang and Vincent Leroy and Yohann Cabon and Boris Chidlovskii and Jerome Revaud}, |
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booktitle = {CVPR}, |
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year = {2024} |
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} |
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``` |
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# License |
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The code is distributed under the CC BY-NC-SA 4.0 License. See [LICENSE](https://github.com/naver/mast3r/blob/main/LICENSE) for more information. |
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For the checkpoints, make sure to agree to the license of all the public training datasets and base checkpoints we used, in addition to CC-BY-NC-SA 4.0. |
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The mapfree dataset license in particular is very restrictive. For more information, check [CHECKPOINTS_NOTICE](https://github.com/naver/mast3r/blob/main/CHECKPOINTS_NOTICE). |
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# Model info |
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Gihub page: https://github.com/naver/mast3r/ |
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| Modelname | Training resolutions | Head | Encoder | Decoder | |
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|-------------|----------------------|------|---------|---------| |
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| MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_nonmetric | 512x384, 512x336, 512x288, 512x256, 512x160 | CatMLP+DPT | ViT-L | ViT-B | |
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# How to use |
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First, [install mast3r](https://github.com/naver/mast3r?tab=readme-ov-file#installation). |
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To load the model: |
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```python |
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from mast3r.model import AsymmetricMASt3R |
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import torch |
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model = AsymmetricMASt3R.from_pretrained("naver/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_nonmetric") |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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model.to(device) |
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``` |
