Datasets:
The dataset viewer is not available for this subset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
~~~~~~~~~~~~~~~~~~~~~~~~~^
StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 81, in _split_generators
first_examples = list(islice(pipeline, self.NUM_EXAMPLES_FOR_FEATURES_INFERENCE))
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 57, in _get_pipeline_from_tar
current_example[field_name] = cls.DECODERS[data_extension](current_example[field_name])
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 319, in npy_loads
return numpy.lib.format.read_array(stream, allow_pickle=False)
~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/numpy/lib/_format_impl.py", line 833, in read_array
raise ValueError("Object arrays cannot be loaded when "
"allow_pickle=False")
ValueError: Object arrays cannot be loaded when allow_pickle=False
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 66, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
~~~~~~~~~~~~~~~~~~~~~~~^
path=dataset,
^^^^^^^^^^^^^
config_name=config,
^^^^^^^^^^^^^^^^^^^
token=hf_token,
^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
path,
...<6 lines>...
**config_kwargs,
)
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
OmniScene
Photorealistic synthetic dataset (rendered in NVIDIA Isaac Sim) used to train X-Lens. Each scene is captured by a 6-camera rig — 4 fisheye + 2 pinhole — with metric ground-truth depth, so a single sample already contains the pinhole / fisheye / heterogeneous mix the model targets.
Packaging
To stay friendly to the Hub, each scene is a single .tar (contents are already
compressed JPG/PNG/NPY, so the tar is stored uncompressed):
train/<scene>.tar
valid/<scene>.tar
test/<scene>.tar
texture/ # shared fisheye ray-direction LUTs (CAM_A/B/C/D_rayEnterDirection.exr, ...)
texture/ holds the rig's fixed per-camera fisheye LUTs (same for every scene); the fisheye
evaluation reads CAM_*_rayEnterDirection.exr from here.
Each tar extracts to the original per-scene layout:
<scene>/
├── rgb/ CAM_*/<frame>.jpg # 8-bit RGB, 1920x1200
├── depth/ CAM_*/<frame>.png # 16-bit metric depth (see decoding)
├── mask/ CAM_*_mask.png # static per-camera valid-lens mask
├── sky_mask/ _meta.json (+ per-frame masks when sky is present)
└── common/ <frame>.npy # per-frame camera parameters (all 6 views)
The camera rig (6 views, all 1920×1200)
| View | Model | Notes |
|---|---|---|
CAM_A CAM_B CAM_C CAM_D |
fisheye — omnidirectional (Mei / unified-sphere: xi + radtan) |
4 side cameras |
CAM_Front CAM_Back |
pinhole — perspective (3×3 K) |
forward / backward views |
common/<frame>.npy is a pickled dict keyed by camera name; each entry provides
intrinsics (3×3 K), extrinsics_world (4×4 cam-to-world, OpenCV: X right / Y down / Z forward),
extrinsics_camera (relative poses to the other views), and intrinsics_full (full source
calibration: the omni fisheye model for CAM_A/B/C/D; CAM_Front/Back are consumed as pinhole via K).
Decoding depth
Depth is a 16-bit PNG; recover metres by dividing by 256. A value of 0 means
invalid / sky (pair it with sky_mask). The raw 16-bit values are small, so the PNGs look
almost black in a normal image viewer — that is expected, load them numerically:
import cv2, numpy as np
png = cv2.imread("depth/CAM_A/0000_0000.png", cv2.IMREAD_UNCHANGED) # MUST be UNCHANGED (16-bit)
depth_m = png.astype(np.float32) / 256.0 # metres
valid = png > 0 # 0 = invalid / sky
Usage
from huggingface_hub import hf_hub_download
import tarfile
# grab one scene
p = hf_hub_download("henryzhou998/OmniScene", "test/<scene>.tar", repo_type="dataset")
tarfile.open(p).extractall("omniscene/") # -> omniscene/<scene>/{rgb,depth,mask,...}
# or a whole split
from huggingface_hub import snapshot_download
snapshot_download("henryzhou998/OmniScene", repo_type="dataset",
allow_patterns="test/*", local_dir="omniscene_tars")
License
Released for non-commercial research use under CC BY-NC 4.0.
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