metadata

tags:
  - image-to-3d
  - pytorch_model_hub_mixin
  - model_hub_mixin
library_name: mast3r
repo_url: https://github.com/naver/mast3r

Grounding Image Matching in 3D with MASt3R

@misc{mast3r_arxiv24,
      title={Grounding Image Matching in 3D with MASt3R}, 
      author={Vincent Leroy and Yohann Cabon and Jerome Revaud},
      year={2024},
      eprint={2406.09756},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@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}
}

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.
The mapfree dataset license in particular is very restrictive. For more information, check CHECKPOINTS_NOTICE.

Model info

Gihub page: https://github.com/naver/mast3r/

Modelname	Training resolutions	Head	Encoder	Decoder
MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_nonmetric	512x384, 512x336, 512x288, 512x256, 512x160	CatMLP+DPT	ViT-L	ViT-B

How to use

First, install mast3r. To load the model:

from mast3r.model import AsymmetricMASt3R
import torch

model = AsymmetricMASt3R.from_pretrained("naver/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_nonmetric")

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