# lotus DA2: Depth Anything in Any Direction [![Page](https://img.shields.io/badge/Project-Website-pink?logo=googlechrome&logoColor=white)](https://depth-any-in-any-dir.github.io/) [![Paper](https://img.shields.io/badge/arXiv-Paper-b31b1b?logo=arxiv&logoColor=white)](http://arxiv.org/abs/2509.26618) [![HuggingFace Demo](https://img.shields.io/badge/🤗%20HuggingFace-Demo%20-yellow)](https://huggingface.co/spaces/haodongli/DA-2) [![Data](https://img.shields.io/badge/📂%20HuggingFace-Data-green)](https://huggingface.co/datasets/haodongli/DA-2) [![Slides](https://img.shields.io/badge/Google-Slides-blue?logo=slideshare&logoColor=white)](https://docs.google.com/presentation/d/1QUonqLuYGEh0qcqY72pbTXsZimINlyN4rOogy7qX4GY/edit?usp=sharing) [![BibTeX](https://img.shields.io/badge/BibTeX-grey?logo=googlescholar&logoColor=white)](https://depth-any-in-any-dir.github.io/bibtex.txt) [Haodong Li](https://haodong2000.github.io/)123§, [Wangguangdong Zheng](https://wangguandongzheng.github.io/)1, [Jing He](https://jingheya.github.io/)3, [Yuhao Liu](https://yuhaoliu7456.github.io/)1, [Xin Lin](https://linxin0.github.io/)2, [Xin Yang](https://abnervictor.github.io/2023/06/12/Academic-Self-Intro.html)34,
[Ying-Cong Chen](https://www.yingcong.me/)34✉, [Chunchao Guo]()1✉ 1Tencent Hunyuan 2UC San Diego 3HKUST(GZ) 4HKUST
§Work primarily done during an internship at Tencent Hunyuan. ✉Corresponding author. ![teaser](assets/badges/teaser.jpg) DA2 predicts dense, scale-invariant distance from a single 360° panorama in an end-to-end manner, with remarkable geometric fidelity and strong zero-shot generalization. ## 📢 News - 2025-10-10 The curated panoramic data is released on [huggingface](https://huggingface.co/datasets/haodongli/DA-2)! - 2025-10-10 The evaluation code and the [testing data](https://huggingface.co/datasets/haodongli/DA-2-Evaluation) are released! - 2025-10-04 The 🤗Huggingface Gradio demo ([online](https://huggingface.co/spaces/haodongli/DA-2) and [local](https://github.com/EnVision-Research/DA-2?tab=readme-ov-file#-gradio-demo)) are released! - 2025-10-04 The inference code and the [model](https://huggingface.co/haodongli/DA-2) are released! - 2025-10-01 [Paper](https://arxiv.org/abs/2509.26618) released on arXiv! ## 🛠️ Setup > This installation was tested on: Ubuntu 20.04 LTS, Python 3.12, CUDA 12.2, NVIDIA GeForce RTX 3090. 1. Clone the repository: ``` git clone https://github.com/EnVision-Research/DA-2.git cd DA-2 ``` 2. Install dependencies using conda: ``` conda create -n da-2 python=3.12 -y conda activate da-2 pip install -e src ``` > For macOS users: Please remove `xformers==0.0.28.post2` (line 16) from `src/pyproject.toml` before `pip install -e src`, as [xFormers does not support macOS](https://github.com/facebookresearch/xformers/issues/775#issuecomment-1611284979). ## 🤗 Gradio Demo 1. Online demo: [Hugggingface Space](https://huggingface.co/spaces/haodongli/DA-2) 2. Local demo: ``` python app.py ``` ## 🕹️ Inference > We've pre-uploaded the cases appeared in the [project page](https://depth-any-in-any-dir.github.io/). So you can proceed directly to step 3. 1. Images are placed in a directory, e.g., `assets/demos`. 2. (Optional) Masks (e.g., sky masks for outdoor images) in another directory, e.g., `assets/masks`. The filenames under both directories should be consistent. 3. Run the inference command: ``` sh infer.sh ``` 4. The visualized distance and normal maps will be saved at `output/infer/vis_all.png`. The projected 3D point clouds will be saved at `output/infer/3dpc`. ## 🚗 Evaluation 1. Download the evaluation datasets from [huggingface](https://huggingface.co/datasets/haodongli/DA-2-Evaluation): ``` cd [YOUR_DATA_DIR] huggingface-cli login hf download --repo-type dataset haodongli/DA-2-Evaluation --local-dir [YOUR_DATA_DIR] ``` 2. Unzip the downloaded datasets: ``` tar -zxvf [DATA_NAME].tar.gz ``` 3. Set the `datasets_dir` (line 20) in `configs/eval.json` with `YOUR_DATA_DIR`. 4. Run the evaluation command: ``` sh eval.sh ``` 5. The results will be saved at `output/eval`. ## 🎓 Citation If you find our work useful in your research, please consider citing our paper🌹: ```bibtex @article{li2025depth, title={DA$^{2}$: Depth Anything in Any Direction}, 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}, journal={arXiv preprint arXiv:2509.26618}, year={2025} } ``` ## 🤝 Acknowledgement This implementation is impossible without the awesome contributions of [MoGe](https://wangrc.site/MoGePage/), [UniK3D](https://lpiccinelli-eth.github.io/pub/unik3d/), [Lotus](https://lotus3d.github.io/), [Marigold](https://marigoldmonodepth.github.io/), [DINOv2](https://github.com/facebookresearch/dinov2), [Accelerate](https://github.com/huggingface/accelerate), [Gradio](https://github.com/gradio-app/gradio), [HuggingFace Hub](https://github.com/huggingface/huggingface_hub), and [PyTorch](https://pytorch.org/) to the open-cource community.