Test-time training 3D reconstruction

ttt3r.mp4 #

• Clone TTT3R.

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

• 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
conda install 'llvm-openmp<16'
pip install evo
pip install open3d

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

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

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 ..

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

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

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

.

Please refer to the eval.md for more details.

Our code is based on the following awesome repositories:

We thank the authors for releasing their code!

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}
    }