Inception3D/TTT3R

[ICLR 2026] A simple state update rule to enhance length generalization for CUT3R - Inception3D/TTT3R

688 stars · 32 forks · Python Topics: slam, 3d-reconstruction, rnn-model

README

TTT3R: 3D Reconstruction as Test-Time Training

Xingyu Chen, Yue Chen, Yuliang Xiu, Andreas Geiger, Anpei Chen

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

ttt3r.mp4

Getting Started Installation

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

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

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} }

source

Inception3D/TTT3R: [ICLR 2026] A simple state update rule to enhance length generalization for CUT3R - Inception3D/TTT3