metadata

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

@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 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.

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_linear	512x384, 512x336, 512x288, 512x256, 512x160	Linear	ViT-L	ViT-B

How to use

First, install dust3r. To load the model:

from dust3r.model import AsymmetricCroCo3DStereo
import torch

model = AsymmetricCroCo3DStereo.from_pretrained("naver/DUSt3R_ViTLarge_BaseDecoder_512_linear")

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)