Dust3r ViTLarge BaseDecoder 512 Dpt

🚀 DUSt3R: Geometric 3D Vision Made Easy

DUSt3R simplifies geometric 3D vision tasks, offering an efficient solution for image - to - 3D applications.

📚 Documentation

BibTeX References

@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.

📦 Installation

First, install dust3r.

💻 Usage Examples

Basic Usage

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)

📚 Documentation

Model Information

• Gihub page: https://github.com/naver/dust3r/
• Project page: https://dust3r.europe.naverlabs.com/