Depth Anything in 360^circ: Towards Scale Invariance in the Wild
Paper • 2512.22819 • Published
Datasets:
Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: ValueError
Message: Invalid string class label archives
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2386, in __iter__
example = _apply_feature_types_on_example(
example, self.features, token_per_repo_id=self.token_per_repo_id
)
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2303, in _apply_feature_types_on_example
encoded_example = features.encode_example(example)
File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 2178, in encode_example
return encode_nested_example(self, example)
File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1460, in encode_nested_example
{k: encode_nested_example(schema[k], obj.get(k), level=level + 1) for k in schema}
~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1483, in encode_nested_example
return schema.encode_example(obj) if obj is not None else None
~~~~~~~~~~~~~~~~~~~~~^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1158, in encode_example
example_data = self.str2int(example_data)
File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1095, in str2int
output = [self._strval2int(value) for value in values]
~~~~~~~~~~~~~~~~^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1116, in _strval2int
raise ValueError(f"Invalid string class label {value}")
ValueError: Invalid string class label archivesNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
DA360 benchmark test sets
Processed equirectangular (ERP) RGB–depth test splits used to evaluate DA360: Depth Anything in 360° (arXiv:2512.22819 · code · demo).
All subsets are test-only (no train/validation). Each benchmark is shipped as one zip under archives/.
| Dataset | Source | Publication | Num images | Storage size | Note |
|---|---|---|---|---|---|
| Metropolis | Mapillary Metropolis | DA360 Sec. 3.3 | 3,000 | 3.1 GB | New outdoor ERP benchmark. Curated from about 10k synchronized Mapillary samples; quality-filtered; bottom ERP inpainted (UniFuse-style); cubemap depth reprojected to ERP. 16-bit depth, meters = pixel / 512. |
| Matterport3D | Matterport3D | [1] | 2,014 | 2.8 GB | Standard 18-scene indoor test split (same protocol as prior panoramic works). pano_skybox_color/ + pano_depth/. 16-bit depth, meters = pixel / 4000. |
| Stanford2D3D | Stanford 2D-3D-S | [2] | 373 | 0.9 GB | Standard Area 5 test set (area_5a, area_5b). 16-bit depth, meters = pixel / 512. |
Total (all zips): 6.8 GB. 6,387 RGB–depth ERP test pairs across the three benchmarks.
Evaluation in the paper uses 518×1036 ERP resolution. See the official repo for metrics and bash scripts/evaluate.sh.
Download
Install the Hub client if needed:
pip install huggingface_hub
Download the full repository (or only archives/):
hf download hljiang/DA360 --repo-type dataset --local-dir /PATH/TO/DOWNLOAD --local-dir-use-symlinks False
Extract each benchmark into a shared data/ folder (layout expected by antigravity-tech/DA360):
cd /PATH/TO/DOWNLOAD/archives
for z in Metropolis Matterport3D Stanford2D3D; do
unzip -q "${z}.zip" -d ../data
done
One-shot (download zips from the Hub and extract to ./data):
bash -c "$(curl -fsSL https://huggingface.co/datasets/hljiang/DA360/raw/main/scripts/prepare_data.sh)"
Layout after unzip
data/
├── Metropolis/rgb/*.jpg + Metropolis/depth/*.png
├── Matterport3D/{scene}/pano_skybox_color|pano_depth/
└── Stanford2D3D/area_5{a,b}/rgb_|depth/
Reading depth maps
| Dataset | Format | Metric depth |
|---|---|---|
| Metropolis | 16-bit PNG | depth_m = pixel_value / 512 |
| Matterport3D | 16-bit PNG | depth_m = pixel_value / 4000 |
| Stanford2D3D | 16-bit PNG | depth_m = pixel_value / 512 |
Reference loaders: demo/datasets/ in the DA360 repository.
Reference
DA360 (this release). Jiang, H., Song, Z., Lou, Z., Xu, R., Tan, M. Depth Anything in 360°: Towards Scale Invariance in the Wild. arXiv:2512.22819, 2025.
[1] Chang, A., Dai, A., Funkhouser, T., et al. Matterport3D: Learning from RGB-D Data in Indoor Environments. 3DV, 2017.
[2] Armeni, I., Sax, S., Zamir, A. R., Savarese, S. Joint 2D-3D-Semantic Data for Indoor Scene Understanding. arXiv:1702.01105, 2017.
Metropolis raw data. Mapillary Metropolis dataset (2024). Used under the curation pipeline described in DA360 supplementary material (Sec. S1).
Citation
If you use these benchmarks, please cite the DA360 paper and the original dataset sources above.
- Downloads last month
- 24