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 84, in _split_generators
raise ValueError(
...<2 lines>...
)
ValueError: The TAR archives of the dataset should be in WebDataset format, but the files in the archive don't share the same prefix or the same types.
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 68, 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.
XRStereo
Synthetic stereo video dataset introduced with StereoSplat: Metric-Scale Novel View Synthesis via Stereo-Grounded Gaussian Splatting (NeurIPS 2026).
Paper | Project Page | Code
Sequences of rectified stereo pairs rendered from moving cameras in static scenes, with dense disparity for both views, intrinsics, baseline and camera poses. This release is the StereoSplat training split.
| Sequences | 17,922 (train split) |
| Stereo frames per sequence | 5 |
| Resolution | 1024 x 1024, RGB, 8 bit |
| Horizontal field of view | 50 to 130 degrees |
| Stereo baseline | 40 to 120 mm |
| Size | 372.8 GB in 72 tar shards |
Download
hf download facebook/xrstereo --repo-type dataset --local-dir xrstereo
cd xrstereo
for t in data/*.tar; do tar -xf "$t"; done
manifest.json lists the sequences, size and SHA-256 of each shard.
Structure
sequences/
seq_000000/
calibration.json {"f", "cx", "cy", "baseline_m"}
poses_left.npy (5, 4, 4) float64, world-from-camera pose of the left camera
frame_0000.npz ... frame_0004.npz, in temporal order
...
splits/train.txt one sequence id per line
Each frame_XXXX.npz holds:
| Key | dtype, shape | Content |
|---|---|---|
image_left, image_right |
uint8, (1024, 1024, 3) | RGB images |
disparity_left |
float16, (1024, 1024) | left pixel x matches right pixel x - d |
disparity_right |
float16, (1024, 1024) | right pixel x matches left pixel x + d |
Disparity <= 0 is invalid (e.g. sky). Disparity is measured through transparent surfaces such as glass.
Cameras
- Both cameras share the intrinsics
K = [[f, 0, cx], [0, f, cy], [0, 0, 1]](pixels), constant over the sequence. - The right camera is the left camera translated by
baseline_mmetres along its x axis. - Metric depth:
depth = f * baseline_m / disparity. poses_left.npyis in metres, in Blender camera axes (x right, y up, z backward). For OpenCV axes, multiply on the right bydiag(1, -1, -1, 1).
import json
import numpy as np
seq = "sequences/seq_000000"
calib = json.load(open(f"{seq}/calibration.json"))
c2w_left = np.load(f"{seq}/poses_left.npy") @ np.diag([1.0, -1.0, -1.0, 1.0]) # OpenCV
with np.load(f"{seq}/frame_0000.npz") as z:
disp = z["disparity_left"].astype(np.float32)
depth = np.where(disp > 0, calib["f"] * calib["baseline_m"] / np.maximum(disp, 1e-8), 0.0)
License
CC BY-NC 4.0, see LICENSE. Non-commercial use only.
Citation
@inproceedings{yugay2026stereosplat,
title = {StereoSplat: Metric-Scale Novel View Synthesis via Stereo-Grounded Gaussian Splatting},
author = {Yugay, Vladimir and Rozumny, Denis and Gevers, Theo and Vansteenkiste, Elias and Oswald, Martin R. and Luvizon, Diogo C.},
booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
year = {2026}
}
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