3D Reconstruction
Collection
DUSt3R/MASt3R models
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5 items
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Updated
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1
@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 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 for details.
Gihub page: https://github.com/naver/dust3r/ Project page: https://dust3r.europe.naverlabs.com/
Modelname | Training resolutions | Head | Encoder | Decoder |
---|---|---|---|---|
DUSt3R_ViTLarge_BaseDecoder_224_linear | 224x224 | Linear | 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_224_linear")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)