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  ---
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- library_name: dust3r
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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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  repo_url: https://github.com/naver/dust3r
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  ---
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- This model has been pushed to the Hub using **dust3r**:
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- - Repo: https://github.com/naver/dust3r
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- - Docs: [More Information Needed]
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
 
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  tags:
 
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  - pytorch_model_hub_mixin
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  - model_hub_mixin
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+ - image-to-3d
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+ library_name: dust3r
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  repo_url: https://github.com/naver/dust3r
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  ---
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+ ## DUSt3R: Geometric 3D Vision Made Easy
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+
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+ ```bibtex
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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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+
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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/dust3r/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. See [section: Our Hyperparameters](https://github.com/naver/dust3r?tab=readme-ov-file#our-hyperparameters) for details.
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+
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+ # Model info
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+
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+ Gihub page: https://github.com/naver/dust3r/
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+ Project page: https://dust3r.europe.naverlabs.com/
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+
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+ | Modelname | Training resolutions | Head | Encoder | Decoder |
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+ |-------------|----------------------|------|---------|---------|
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+ | DUSt3R_ViTLarge_BaseDecoder_512_linear | 512x384, 512x336, 512x288, 512x256, 512x160 | Linear | ViT-L | ViT-B |
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+
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+ # How to use
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+
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+ First, [install dust3r](https://github.com/naver/dust3r?tab=readme-ov-file#installation). Make sure to install the huggingface-hub[torch]>=0.22 optional dependency.
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+ To load the model:
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+
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+ ```python
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+ from dust3r.model import AsymmetricCroCo3DStereo
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+ import torch
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+
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+ model = AsymmetricCroCo3DStereo.from_pretrained("naver/DUSt3R_ViTLarge_BaseDecoder_512_linear")
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+
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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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+ ```