| # <img src="assets/badges/icon2.png" alt="lotus" style="height:1.2em; vertical-align:bottom;"/> DA<sup>2</sup>: Depth Anything in Any Direction | |
| [](https://depth-any-in-any-dir.github.io/) | |
| [](http://arxiv.org/abs/2509.26618) | |
| [](https://huggingface.co/spaces/haodongli/DA-2) | |
| [](https://huggingface.co/datasets/haodongli/DA-2) | |
| [](https://docs.google.com/presentation/d/1QUonqLuYGEh0qcqY72pbTXsZimINlyN4rOogy7qX4GY/edit?usp=sharing) | |
| [](https://depth-any-in-any-dir.github.io/bibtex.txt) | |
| [Haodong Li](https://haodong2000.github.io/)<sup>123§</sup>, | |
| [Wangguangdong Zheng](https://wangguandongzheng.github.io/)<sup>1</sup>, | |
| [Jing He](https://jingheya.github.io/)<sup>3</sup>, | |
| [Yuhao Liu](https://yuhaoliu7456.github.io/)<sup>1</sup>, | |
| [Xin Lin](https://linxin0.github.io/)<sup>2</sup>, | |
| [Xin Yang](https://abnervictor.github.io/2023/06/12/Academic-Self-Intro.html)<sup>34</sup>,<br> | |
| [Ying-Cong Chen](https://www.yingcong.me/)<sup>34✉</sup>, | |
| [Chunchao Guo]()<sup>1✉</sup> | |
| <span class="author-block"><sup>1</sup>Tencent Hunyuan</span> | |
| <span class="author-block"><sup>2</sup>UC San Diego</span> | |
| <span class="author-block"><sup>3</sup>HKUST(GZ)</span> | |
| <span class="author-block"><sup>4</sup>HKUST</span><br> | |
| <span class="author-block"> | |
| <sup>§</sup>Work primarily done during an internship at Tencent Hunyuan. | |
| <sup>✉</sup>Corresponding author. | |
| </span> | |
|  | |
| <strong>DA<sup>2</sup> 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.</strong> | |
| ## 📢 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. | |