StereoSpace: Depth-Free Synthesis of Stereo Geometry via End-to-End Diffusion in a Canonical Space

Tjark Behrens1, Anton Obukhov3, Bingxin Ke1, Fabio Tosi2, Matteo Poggi2, Konrad Schindler1
1ETH Zurich   |   2University of Bologna   |   3Huawei Bayer Lab

This repository is the official implementation of the paper titled "StereoSpace: Depth-Free Synthesis of Stereo Geometry via End-to-End Diffusion in a Canonical Space".

Quick Start

Environment & Requirements

Create and activate the environment:

git clone https://github.com/prs-eth/stereospace.git
cd stereospace
python -m venv ~/venv_stereospace
source ~/venv_stereospace/bin/activate
pip install -r requirements.txt

Inference

python inference.py

This will:

• ⬇️ Download the necessary checkpoints. If you are prompted to log in, please provide a read access token from Hugging Face → Settings → Access Tokens. When asked 'Add token as git credential? (Y/n)', select 'n'.
• 👀 Create stereo from input images; without specifying --input, it will use the example_images directory.
• 💾 Save predictions to an output folder.

You can also pass the following arguments:

• --input INPUT: Input image or a directory path, default ./example_images;
• --output OUTPUT: Output directory, default ./outputs;
• --baseline BASELINE: Baseline, default 0.15 (15 cm);
• --batch_size BATCH_SIZE: Batch size when processing a folder of images, default is 1;
• --src_intrinsics, --tgt_intrinsics: Camera intrinsics for precise control of the FOV, default is a standard camera.

Troubleshooting

Problem	Solution
(pip) Errors installing requirements via pip install -r requirements.txt	python -m pip install --upgrade pip

Citation

Please cite our paper:

@misc{behrens2025stereospace,
  title        = {StereoSpace: Depth-Free Synthesis of Stereo Geometry via End-to-End Diffusion in a Canonical Space},
  author       = {Tjark Behrens and Anton Obukhov and Bingxin Ke and Fabio Tosi and Matteo Poggi and Konrad Schindler},
  year         = {2025},
  eprint       = {2512.10959},
  archivePrefix= {arXiv},
  primaryClass = {cs.CV},
  url          = {https://arxiv.org/abs/2512.10959},
}

License

The code and models of this work are licensed under the MIT License. By downloading and using the code and model you agree to the terms in LICENSE.