| --- |
| 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 |
|
|
| [](https://depth-any-in-any-dir.github.io/) |
| [](http://arxiv.org/abs/2509.26618) |
| [](https://github.com/EnVision-Research/DA-2) |
| [](https://huggingface.co/spaces/haodongli/DA-2) |
|
|
| <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> |
|
|
|  |
|
|
| # ⬇️ 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° 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} |
| } |
| ``` |
|
|
