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  - depth-estimation
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  - computer-vision
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  ---
 
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  # ORB: Omni-directional Reconstruction Backbone
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  ![ORB Banner](https://raw.githubusercontent.com/speridlabs/ORB/main/assets/banner.png)
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- **ORB (Omni-directional Reconstruction Backbone)** is a state-of-the-art 360° panorama depth estimation model that predicts dense, scale-invariant distance from a single equirectangular panorama in an end-to-end manner.
 
 
 
 
 
 
 
 
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- ## 🎯 Key Features
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- - **360° Depth Estimation**: Predicts dense depth maps from equirectangular panoramas
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- - **Scale-Invariant**: Robust distance predictions with geometric fidelity
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- - **Zero-Shot Generalization**: Strong performance on unseen data
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- - **End-to-End**: Single forward pass for complete panorama depth
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- - **Easy Integration**: Simple Python API with HuggingFace support
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- ## 🚀 Quick Start
 
 
 
 
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- For using the model go to the [ORB GitHub Repository](https://github.com/speridlabs/orb)
 
 
 
 
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- ## 📁 Model Files
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- - `model.safetensors` - Main model weights (1.3 GB)
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- - `README.md` - This file
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- ## 🎓 Citation
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- **ORB is based on DA²: Depth Anything in Any Direction**
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  ```bibtex
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- @article{li2025depth,
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- title={DA $^{2}$: Depth Anything in Any Direction},
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- author={Li, Haodong and Zheng, Wangguangdong and He, Jing and Liu, Yuhao and Lin, Xin and Yang, Xin and Chen, Ying-Cong and Guo, Chunchao},
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- journal={arXiv preprint arXiv:2509.26618},
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- year={2025}
 
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  }
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  ```
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- ## 📜 License
 
 
 
 
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- Apache 2.0 License - See [LICENSE](https://github.com/speridlabs/ORB/blob/main/LICENSE) for details.
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  ---
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  - depth-estimation
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  - computer-vision
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  ---
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+
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  # ORB: Omni-directional Reconstruction Backbone
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  ![ORB Banner](https://raw.githubusercontent.com/speridlabs/ORB/main/assets/banner.png)
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+ **ORB** is a 360° panorama depth estimation model that predicts dense distance maps from equirectangular panoramas in a single forward pass.
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+
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+ ## Model Description
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+
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+ This model takes a 360° equirectangular panorama (2:1 aspect ratio) as input and outputs a dense depth/distance map at the same resolution. It's designed for:
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+ - **Zero-shot depth estimation** from panoramic images
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+ - **Scale-invariant predictions** with geometric fidelity
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+ - **End-to-end processing** without post-processing
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+ ## Quick Start
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+ ```python
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+ from orb import predict_pano_depth
 
 
 
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+ # Predict depth from panorama
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+ distance = predict_pano_depth('panorama.png')
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+ ```
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+
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+ ## Model Details
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+ - **Input**: RGB panorama (equirectangular, width = 2 × height)
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+ - **Output**: Dense depth/distance map (same resolution as input)
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+ - **Format**: SafeTensors (1.3 GB)
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+ - **Precision**: FP32 / FP16 supported
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+ - **Base Architecture**: Built upon [DA²: Depth Anything in Any Direction](https://arxiv.org/abs/2509.26618)
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+ ## 📖 Full Documentation
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+ For complete installation instructions, advanced usage, API documentation, and examples, please visit:
 
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+ **[github.com/speridlabs/ORB](https://github.com/speridlabs/ORB)**
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+ ## Citation
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  ```bibtex
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+ @misc{orb2024,
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+ title={ORB: Omni-directional Reconstruction Backbone},
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+ author={Sperid Labs and Del Pino, Javier and Garabito, Chema and Sanchez, Alejandro},
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+ year={2024},
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+ publisher={Sperid Labs},
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+ url={https://github.com/speridlabs/ORB}
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  }
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  ```
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+ ## License
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+
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+ Apache 2.0 - See [LICENSE](https://github.com/speridlabs/ORB/blob/main/LICENSE)
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+
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+ ## Acknowledgements
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+ Built upon the foundational work of the [DA-2](https://arxiv.org/abs/2509.26618).
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  ---
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