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

${{\color{Red}\Huge{\textsf{ CVPR\ 2025\ }}}}$

Official implementation of Fast3R: Towards 3D Reconstruction of 1000+ Images in One Forward Pass, CVPR 2025

Jianing Yang, Alexander Sax, Kevin J. Liang, Mikael Henaff, Hao Tang, Ang Cao, Joyce Chai, Franziska Meier, Matt Feiszli

Installation

# clone project
git clone https://github.com/facebookresearch/fast3r
cd fast3r

# create conda environment
conda create -n fast3r python=3.11 cmake=3.14.0 -y
conda activate fast3r

# install PyTorch (adjust cuda version according to your system)
conda install pytorch torchvision torchaudio pytorch-cuda=12.4 nvidia/label/cuda-12.4.0::cuda-toolkit -c pytorch -c nvidia

# install PyTorch3D from source (the compilation will take a while)
pip install "git+https://github.com/facebookresearch/pytorch3d.git@stable"

# install requirements
pip install -r requirements.txt

# install fast3r as a package (so you can import fast3r and use it in your own project)
pip install -e .

Installation Troubleshooting

If you encounter the error fatal error: cuda_runtime.h: No such file or directory when installing PyTorch3D, try setting CUDA_HOME before installing PyTorch3D:

export CUDA_HOME=/usr/local/cuda-12.4
pip install "git+https://github.com/facebookresearch/pytorch3d.git@stable"

Demo

Use the following command to run the demo:

python fast3r/viz/demo.py

This will automatically download the pre-trained model weights and config from Hugging Face Model.

The demo is a Gradio interface where you can upload images or a video and visualize the 3D reconstruction and camera pose estimation.

fast3r/viz/demo.py also serves as an example of how to use the model for inference.

Left: Upload a video. Right: Visualize the 3D Reconstruction

Click here to see example of: visualize confidence heatmap + play frame by frame + render a GIF

Using Fast3R in Your Own Project

To use Fast3R in your own project, you can import the Fast3R class from fast3r.models.fast3r 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()

Training

Train model with chosen experiment configuration from configs/experiment/

python fast3r/train.py experiment=super_long_training/super_long_training

You can override any parameter from command line following Hydra override syntax:

python fast3r/train.py experiment=super_long_training/super_long_training trainer.max_epochs=20 trainer.num_nodes=2

To submit a multi-node training job with Slurm, use the following command:

python scripts/slurm/submit_train.py --nodes=<NODES> --experiment=<EXPERIMENT>

Evaluation

To evaluate on 3D reconstruction or camera pose estimation tasks, run:

python fast3r/eval.py eval=<eval_config>

<eval_config> can be any of the evaluation configurations in configs/eval/. For example:

• ablation_recon_better_inference_hp/ablation_recon_better_inference_hp evaluates the 3D reconstruction on DTU, 7-Scenes and Neural-RGBD datasets.
• eval_cam_pose/eval_cam_pose_10views evaluates the camera pose estimation on 10 views on CO3D dataset.

To evaluate camera poses on RealEstate10K dataset, run:

python scripts/fast3r_re10k_pose_eval.py  --subset_file scripts/re10k_test_1800.txt

To evaluate multi-view depth estimation on Tanks and Temples, ETH-3D, DTU and ScanNet datasets, follow the data download and preparation guide of robustmvd, install that repo's requirements.txt into the current conda environment, and run:

python scripts/robustmvd_eval.py

License

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

Contributing

See contributing and the code of conduct.

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},
}

Acknowledgement

Fast3R is built upon a foundation of remarkable open-source projects. We deeply appreciate the contributions of these projects and their communities, whose efforts have significantly advanced the field and made this work possible.

• DUSt3R
• Spann3R
• Viser
• Lightning-Hydra-Template

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