---
license: apache-2.0
task_categories:
- depth-estimation
tags:
- depth-estimation
- panorama
- 360-depth
- 360-depth-estimation
- 360-image
---

# DA<sup>2</sup>: 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)
[![GitHub](https://img.shields.io/github/stars/EnVision-Research/DA-2?style=default&label=GitHub%20Repo%20★&logo=github)](https://github.com/EnVision-Research/DA-2)
[![HuggingFace Demo](https://img.shields.io/badge/🤗%20HuggingFace-Demo%20-yellow)](https://huggingface.co/spaces/haodongli/DA-2)

<strong>DA<sup>2</sup> predicts dense, scale-invariant distance from a single 360&deg; panorama in an end-to-end manner, with remarkable geometric fidelity and strong zero-shot generalization.</strong>

![teaser](assets/teaser.jpg)

# ⬇️ Download
1. Download the datasets (please see [here](https://github.com/EnVision-Research/DA-2#%EF%B8%8F-setup) for the environment setup):
```
cd [YOUR_DATA_DIR]
huggingface-cli login
hf download --repo-type dataset haodongli/DA-2 --local-dir [YOUR_DATA_DIR]
```
2. Merge parts into one `*.tar.gz` file:
> `DATASET_NAME` in [`hypersim_pano`, `vkitti_pano`, `mvs_synth_pano`, `unreal4k_pano`, `3d-ken-burns_pano`, `dynamic_replica_v2_pano`]
```
cat [DATASET_NAME]/part_* > [DATASET_NAME].tar.gz
```
3. Check the `MD5`:
```
md5sum -c [DATASET_NAME]_checksum.md5
```
4. If correct, then we can unzip it:
```
tar -zxvf [DATASET_NAME].tar.gz
```
5. The data samples will be exported in `[DATASET_NAME]/`.

# 🎮 Usage
1. The dietance values from the pixel to the 360&deg; camera is stored in `depth.png`. I also provided `depth_vis.png` just for visualization.
2. Please refer the code below to load the depth values from `depth.png`:
```
depth = cv2.imread('path/to/depth.png', cv2.IMREAD_UNCHANGED)
depth = depth.astype(np.float32)
depth = depth[:,:,0]
depth = depth * SCALE
depth = torch.from_numpy(depth)
```
3. Please see the below table for the `SCALE` of different curated dataset:
|Curated dataset | Scale |
|:---:|:---:|
|Hypersim | `40.0 / 65535.0` |
|VKITTI, MVS-Synth, 3D-Ken-Burns | `1.0 / 256.0` |
|UnrealStereo4K | `80.0 / 65535.0`|
| DynamicReplica| `20.0 / 65535.0` |
4. The valid masks of the depth maps can be obtained via:
```
valid_mask = torch.logical_and(
    (depth > 1e-5), (depth < 80.0)
).bool()
```

# 🎓 Citation

If you find these datasets useful, please consider citing 🌹:

```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}
}

@inproceedings{roberts2021hypersim,
  title={Hypersim: A photorealistic synthetic dataset for holistic indoor scene understanding},
  author={Roberts, Mike and Ramapuram, Jason and Ranjan, Anurag and Kumar, Atulit and Bautista, Miguel Angel and Paczan, Nathan and Webb, Russ and Susskind, Joshua M},
  booktitle={Proceedings of the IEEE/CVF international conference on computer vision},
  pages={10912--10922},
  year={2021}
}

@article{cabon2020virtual,
  title={Virtual kitti 2},
  author={Cabon, Yohann and Murray, Naila and Humenberger, Martin},
  journal={arXiv preprint arXiv:2001.10773},
  year={2020}
}

@inproceedings{huang2018deepmvs,
  title={Deepmvs: Learning multi-view stereopsis},
  author={Huang, Po-Han and Matzen, Kevin and Kopf, Johannes and Ahuja, Narendra and Huang, Jia-Bin},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={2821--2830},
  year={2018}
}

@inproceedings{tosi2021smd,
  title={Smd-nets: Stereo mixture density networks},
  author={Tosi, Fabio and Liao, Yiyi and Schmitt, Carolin and Geiger, Andreas},
  booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
  pages={8942--8952},
  year={2021}
}

@article{niklaus20193d,
  title={3d ken burns effect from a single image},
  author={Niklaus, Simon and Mai, Long and Yang, Jimei and Liu, Feng},
  journal={ACM Transactions on Graphics (ToG)},
  volume={38},
  number={6},
  pages={1--15},
  year={2019},
  publisher={ACM New York, NY, USA}
}

@inproceedings{karaev2023dynamicstereo,
  title={Dynamicstereo: Consistent dynamic depth from stereo videos},
  author={Karaev, Nikita and Rocco, Ignacio and Graham, Benjamin and Neverova, Natalia and Vedaldi, Andrea and Rupprecht, Christian},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={13229--13239},
  year={2023}
}
```