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Fast3R ViT Large 512

fair-nc

TipoOtherVersiónRevision a2c770bUnratedcalidad

Fuente del modelo

Tipo
Other
Tamaño del archivo
2,41 GB
Versión
vRevision a2c770b
Actualizado
4 nov 2026
Descargas
2,4 mil
Fuente
Hugging Face

Descripción de la fuente

⚡️Fast3R - Towards 3D Reconstruction of 1000+ Images in One Forward Pass

CVPR 2025

Project Website Paper GitHub Repo Gradio Demo Hugging Face Model

Using Fast3R in Your Own Project

To use Fast3R in your own project, you can import the Fast3R class from fast3r.models.fast3r (follow instructions from the Fast3R GitHub repo to install) and use it as a regular PyTorch model.

from fast3r.models.fast3r import Fast3R
from fast3r.models.multiview_dust3r_module import MultiViewDUSt3RLitModule

# Load the model from Hugging Face
model = Fast3R.from_pretrained("jedyang97/Fast3R_ViT_Large_512")
model = model.to("cuda")

# [Optional] Create a lightweight lightning module wrapper for the model.
# This provides functions to estimate camera poses, evaluate 3D reconstruction, etc.
# See fast3r/viz/demo.py for an example.
lit_module = MultiViewDUSt3RLitModule.load_for_inference(model)

# Set model to evaluation mode
model.eval()
lit_module.eval()

Citation

@InProceedings{Yang_2025_Fast3R,
    title={Fast3R: Towards 3D Reconstruction of 1000+ Images in One Forward Pass},
    author={Jianing Yang and Alexander Sax and Kevin J. Liang and Mikael Henaff and Hao Tang and Ang Cao and Joyce Chai and Franziska Meier and Matt Feiszli},
    booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month={June},
    year={2025},
}

License

The code and models are licensed under the FAIR NC Research License.

Fuentes

1 fuente
Hugging FacePrincipalVersión a2c770b768ceb3a53c36c4f7a3619db0413dc3a1
huggingface.co/jedyang97/Fast3R_ViT_Large_512/tree/a2c770b768ceb3a53c36c4f7a3619db0413dc3a1

Verificado 4 nov

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File inventory only; completeness is unverified.

Artefactos del modelo

1 artefacto

model.safetensors

safetensors · FP32 (tensor header) · 2,41 GB · SHA-256 1357d42e7453…0427 · Hugging Face

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Extractos de fuentes

3 extractos

--- license: other license_name: fair-nc license_link: LICENSE tags: - image-to-3d - model_hub_mixin - pytorch_model_hub_mixin library_name: fast3r repo_url: https://github.com/facebookresearch/fast3r --- # ⚡️Fast3R - Towards 3D Reconstruction of 1000+ Images in One Forward Pass *CVPR 2025* [![Project Website](https://img.shields.io/badge/Fast3R-Website-4CAF50?logo=googlechrome&logoColor=white)](https://fast3r-3d.github.io/) [![Paper](https://img.shields.io/badge/arXiv-Paper-b31b1b?logo=arxiv&logoColor=b31b1b)](https://arxiv.org/abs/2501.13928) [![GitHub Repo](https://img.shields.io/badge/GitHub-Code-FFD700?logo=github)](https://github.com/facebookresearch/fast3r) [![Gradio Demo](https://img.shields.io/badge/Gradio-Demo-orange?style=flat&logo=Gradio&logoColor=red)](https://fast3r.ngrok.app/) [![Hugging Face Model](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Model-blue)](https://huggingface.co/jedyang97/Fast3R_ViT_Large_512/) ## Using Fast3R in Your Own Project To use Fast3R in your own project, you can import the `Fast3R` class from `fast3r.models.fast3r` (follow instructions from the [Fast3R GitHub repo](https://github.com/facebookresearch/fast3r) to install) and use it as a regular PyTorch model. ```python from fast3r.models.fast3r import Fast3R from fast3r.models.multiview_dust3r_module import MultiViewDUSt3RLitModule # Load the model from Hugging Face model = Fast3R.from_pretrained("jedyang97/Fast3R_ViT_Large_512") model = model.to("cuda") # [Optional] Create a lightweight lightning module wrapper for the model. # This provides functions to estimate camera poses, evaluate 3D reconstruction, etc. # See fast3r/viz/demo.py for an example. lit_module = MultiViewDUSt3RLitModule.load_for_inference(model) # Set model to evaluation mode model.eval() lit_module.eval() ``` ## Citation ``` @InProceedings{Yang_2025_Fast3R, title={Fast3R: Towards 3D Reconstruction of 1000+ Images in One Forward Pass}, author={Jianing Yang and Alexander Sax and Kevin J. Liang and Mikael Henaff and Hao Tang and Ang Cao and Joyce Chai and Franziska Meier and Matt Feiszli}, booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month={June}, year={2025}, } ``` ## License The code and models are licensed under the [FAIR NC Research License](LICENSE).

Source context: 2412 downloads · 29 likes · Pipeline image-to-3d · Library fast3r · Repo jedyang97/Fast3R_ViT_Large_512

Source context: 2356 downloads · 28 likes · Pipeline image-to-3d · Library fast3r · Repo jedyang97/Fast3R_ViT_Large_512