⚡ GeoBench: 3D Geometry Estimation Made Easy

🔰 Project Page, 📑 Paper

Yongtao Ge^1,2, Guangkai Xu¹, Zhiyue Zhao¹, Libo Sun², Zheng Huang¹, Yanlong Sun³, Hao Chen¹, Chunhua Shen¹

¹Zhejiang University,   ²The University of Adelaide,   ³Tsinghua University  

This toolbox streamlines the use and evaluation for state-of-the-art discriminative and generative geometry estimation models, which can be served as foundation models for various downstream 3D reconstruction applications, including:

• Metric3D-V2
• UniDepth
• Depth-Anything-V2
• Depth-Anything
• DSINE
• Marigold
• DMP
• Genpercept
• Geowizard
• DepthFM

Install

pip install -r requirements.txt
pip install -e . -v

Inference Demos

# inference Marigold
sh scripts/run_marigold.sh

# inference Metric3D
sh scripts/run_metric3d.sh

# inference Depth-Anything
sh scripts/run_depthanything.sh

# inference GenPercept
sh scripts/run_genpercept.sh

# inference DSINE
sh scripts/run_dsine.sh

Benchmarks and Model Zoo

Stay tuned, comming soon.

License

For non-commercial academic use, this project is licensed under the 2-clause BSD License. For commercial use, please contact Chunhua Shen. Note that any third-party software/library involved in this project is licensed under its own license.

Citation

If you find the toolbox useful for your project, please cite our paper:

@article{ge2024geobench,
    title={GeoBench: Benchmarking and Analyzing Monocular Geometry Estimation Models},
    author={Ge, Yongtao and Xu, Guangkai, and Zhao, Zhiyue and Huang, zheng and Sun, libo and Sun, Yanlong and Chen, Hao and Shen, Chunhua},
    journal={arXiv preprint arXiv:2406.12671},
    year={2024}
}