DUSt3R ViTLarge BaseDecoder 224 Linear

Model info

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

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_224_linear")

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_224_linear | 224x224 | Linear | 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_224_linear") device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model.to(device) ```