OpenStereo: A Comprehensive Benchmark for Stereo Matching and Strong Baseline

OpenStereo is a flexible and extensible project for stereo matching.

What's New

• [Nov 22nd, 2024]: The paper of StereoAnything makes public: Stereo Anything: Unifying Stereo Matching with Large-Scale Mixed Data.
• [Oct 31st, 2024]: The code of StereoBase is available.
• [July 11th, 2024]: The code of LightStereo is available.
• [July 1st, 2024]: The paper of LightStereo makes public: LightStereo: Channel Boost Is All Your Need for Efficient 2D Cost Aggregation.
• [June 26th, 2024]: TensorRT has been integrated, please see the Deployment documentation.
• [May 2024]: The 2.0 version of OpenStereo is available, featuring an optimized training and testing framework.
• [January 2024]: Our proposed StereoBase rank 1st on the KITTI15 leaderboard!!!
• [December 2023]: Our paper makes public: OpenStereo: A Comprehensive Benchmark for Stereo Matching and Strong Baseline.
• [March 2023]:OpenStereo is available!!!

Our Publications

• [Arxiv'24] Stereo Anything: Unifying Stereo Matching with Large-Scale Mixed Data, Paper and ProjectPage
• [Arxiv'24] LightStereo: Channel Boost Is All Your Need for Efficient 2D Cost Aggregation, Paper and Code.
• [Arxiv'23] OpenStereo: A Comprehensive Benchmark for Stereo Matching and Strong Baseline, Paper and Code.

Overall

Highlighted features

• Multiple Dataset supported: OpenStereo supports 15 popular stereo datasets: SceneFlow, KITTI12 & KITTI15, ETH3D, Middlebury, DrivingStereo, Sintel, FallingThings, InStereo2K,UnrealStereo4k, VirtualKitti2, CREStereo, Argoverse, Spring, and TartanAir.
• Multiple Models Support: We reproduced several SOTA methods and achieved the same or even better performance.
• DDP Support: The officially recommended Distributed Data Parallel (DDP) mode is used during both the training and testing phases.
• AMP Support: The Auto Mixed Precision (AMP) option is available.
• TensorRT Support: TensorRT has been integrated.
• Nice log: We use tensorboard and logging to log everything, which looks pretty.

Getting Started

Please see 0.get_started.md. We also provide the following tutorials for your reference:

• Prepare dataset
• Detailed configuration
• Customize model
• Advanced usages

Model Zoo

Results and models are available in the model zoo.

Acknowledgement

AANet   ACVNet   CascadeStereo   CFNet   COEX   DenseMatching   FADNet++   GwcNet   MSNet   PSMNet   RAFT   STTR   OpenGait   IGEV   NMRF  

Citation

@article{OpenStereo,
        title={OpenStereo: A Comprehensive Benchmark for Stereo Matching and Strong Baseline},
        author={Guo, Xianda and Zhang, Chenming and Lu, Juntao  and Wang, Yiqi and Duan, Yiqun and Yang, Tian and Zhu, Zheng and Chen, Long},
        journal={arXiv preprint arXiv:2312.00343},
        year={2023}
}
@article{guo2024lightstereo,
        title={LightStereo: Channel Boost Is All Your Need for Efficient 2D Cost Aggregation},
        author={Guo, Xianda and Zhang, Chenming and Nie, Dujun and Zheng, Wenzhao and Zhang, Youmin and Chen, Long},
        journal={arXiv preprint arXiv:2406.19833},
        year={2024}
}
@article{guo2024stereo,
  title={Stereo Anything: Unifying Stereo Matching with Large-Scale Mixed Data},
  author={Guo, Xianda and Zhang, Chenming and Zhang, Youmin and Nie, Dujun and Wang, Ruilin and Zheng, Wenzhao and Poggi, Matteo and Chen, Long},
  journal={arXiv preprint arXiv:2411.14053},
  year={2024}
}

Note: This code is only used for academic purposes, people cannot use this code for anything that might be considered commercial use.