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@inproceedings{dust3rcvpr24, title={DUSt3R: Geometric 3D Vision Made Easy}, author={Shuzhe Wang and Vincent Leroy and Yohann Cabon and Boris Chidlovskii and Jerome Revaud}, booktitle = {CVPR}, year = {2024} }
Modellquelle
Quellenbeschreibung
@inproceedings{dust3r_cvpr24,
title={DUSt3R: Geometric 3D Vision Made Easy},
author={Shuzhe Wang and Vincent Leroy and Yohann Cabon and Boris Chidlovskii and Jerome Revaud},
booktitle = {CVPR},
year = {2024}
}
@misc{dust3r_arxiv23,
title={DUSt3R: Geometric 3D Vision Made Easy},
author={Shuzhe Wang and Vincent Leroy and Yohann Cabon and Boris Chidlovskii and Jerome Revaud},
year={2023},
eprint={2312.14132},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2312.14132},
}
The code is distributed under the CC BY-NC-SA 4.0 License. See for more information. 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 for details.
Quellen
1 QuelleVerifiziert 1. Sept.
Modellartefakte
1 ArtefaktQuellenauszüge
2 AuszügeGihub page: https://github.com/naver/dust3r/ Project page: https://dust3r.europe.naverlabs.com/
| Modelname | Training resolutions | Head | Encoder | Decoder |
|---|---|---|---|---|
| DUSt3R_ViTLarge_BaseDecoder_512_dpt | 512x384, 512x336, 512x288, 512x256, 512x160 | DPT | ViT-L | ViT-B |
First, install dust3r. To load the model:
from dust3r.model import AsymmetricCroCo3DStereo
import torch
model = AsymmetricCroCo3DStereo.from_pretrained("naver/DUSt3R_ViTLarge_BaseDecoder_512_dpt")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
--- tags: - pytorch_model_hub_mixin - model_hub_mixin - image-to-3d library_name: dust3r repo_url: https://github.com/naver/dust3r --- ## DUSt3R: Geometric 3D Vision Made Easy ```bibtex @inproceedings{dust3r_cvpr24, title={DUSt3R: Geometric 3D Vision Made Easy}, author={Shuzhe Wang and Vincent Leroy and Yohann Cabon and Boris Chidlovskii and Jerome Revaud}, booktitle = {CVPR}, year = {2024} } @misc{dust3r_arxiv23, title={DUSt3R: Geometric 3D Vision Made Easy}, author={Shuzhe Wang and Vincent Leroy and Yohann Cabon and Boris Chidlovskii and Jerome Revaud}, year={2023}, eprint={2312.14132}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2312.14132}, } ``` # License 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. 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. # Model info Gihub page: https://github.com/naver/dust3r/ Project page: https://dust3r.europe.naverlabs.com/ | Modelname | Training resolutions | Head | Encoder | Decoder | |-------------|----------------------|------|---------|---------| | DUSt3R_ViTLarge_BaseDecoder_512_dpt | 512x384, 512x336, 512x288, 512x256, 512x160 | DPT | ViT-L | ViT-B | # How to use First, [install dust3r](https://github.com/naver/dust3r?tab=readme-ov-file#installation). To load the model: ```python from dust3r.model import AsymmetricCroCo3DStereo import torch model = AsymmetricCroCo3DStereo.from_pretrained("naver/DUSt3R_ViTLarge_BaseDecoder_512_dpt") device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model.to(device) ```
Source context: 18080 downloads · 17 likes · Pipeline image-to-3d · Library dust3r · Repo naver/DUSt3R_ViTLarge_BaseDecoder_512_dpt