github.com/Inception3D/TTT3R @main

TTT3R: 3D Reconstruction as Test-Time Training

[](https://arxiv.org/abs/2509.26645) [](https://rover-xingyu.github.io/TTT3R)   [](https://x.com/RoverXingyu) [](https://bsky.app/profile/xingyu-chen.bsky.social) [Xingyu Chen](https://rover-xingyu.github.io/), [Yue Chen](https://fanegg.github.io/), [Yuliang Xiu](https://xiuyuliang.cn/), [Andreas Geiger](https://www.cvlibs.net/), [Anpei Chen](https://apchenstu.github.io/)

TL;DR: A simple state update rule to enhance length generalization for CUT3R.

https://github.com/user-attachments/assets/b7583837-1f1e-43a4-b281-09f340ee5918

Getting Started

Installation

1. Clone TTT3R.

git clone https://github.com/Inception3D/TTT3R.git
cd TTT3R

1. Create the environment.

conda create -n ttt3r python=3.11 cmake=3.14.0
conda activate ttt3r
conda install pytorch torchvision pytorch-cuda=12.1 -c pytorch -c nvidia  # use the correct version of cuda for your system
pip install -r requirements.txt
# issues with pytorch dataloader, see https://github.com/pytorch/pytorch/issues/99625
conda install 'llvm-openmp<16'
# for evaluation
pip install evo
pip install open3d

1. Compile the cuda kernels for RoPE (as in CroCo v2).

cd src/croco/models/curope/
python setup.py build_ext --inplace
cd ../../../../

Download Checkpoints

CUT3R provide checkpoints trained on 4-64 views: cut3r_512_dpt_4_64.pth.

To download the weights, run the following commands:

cd src
gdown --fuzzy https://drive.google.com/file/d/1Asz-ZB3FfpzZYwunhQvNPZEUA8XUNAYD/view?usp=drive_link
cd ..

Inference Demo

To run the inference demo, you can use the following command:

# input can be a folder or a video
# the following script will run inference with TTT3R and visualize the output with viser on port 8080
CUDA_VISIBLE_DEVICES=6 python demo.py --model_path MODEL_PATH --size 512 \
    --seq_path SEQ_PATH --output_dir OUT_DIR --port 8080 \
    --model_update_type ttt3r --frame_interval 1 --reset_interval 100 \
    --downsample_factor 1000 --vis_threshold 5.0

# Example:
CUDA_VISIBLE_DEVICES=6 python demo.py --model_path src/cut3r_512_dpt_4_64.pth --size 512 \
    --seq_path examples/westlake.mp4 --output_dir tmp/taylor --port 8080 \
    --model_update_type ttt3r --frame_interval 1 --reset_interval 100 \
    --downsample_factor 100 --vis_threshold 6.0

CUDA_VISIBLE_DEVICES=6 python demo.py --model_path src/cut3r_512_dpt_4_64.pth --size 512 \
    --seq_path examples/taylor.mp4 --output_dir tmp/taylor --port 8080 \
    --model_update_type ttt3r --frame_interval 1 --reset_interval 50 \
    --downsample_factor 100 --vis_threshold 10.0 

Output results will be saved to output_dir.

Evaluation

Please refer to the eval.md for more details.

Acknowledgements

Our code is based on the following awesome repositories:

• CUT3R
• Easi3R
• DUSt3R
• MonST3R
• Spann3R
• Viser

We thank the authors for releasing their code!

Citation

If you find our work useful, please cite:

@article{chen2025ttt3r,
    title={TTT3R: 3D Reconstruction as Test-Time Training},
    author={Chen, Xingyu and Chen, Yue and Xiu, Yuliang and Geiger, Andreas and Chen, Anpei},
    journal={arXiv preprint arXiv:2509.26645},
    year={2025}
    }