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: FileNotFoundError
Message: datasets/nvidia/ffs_stereo4d@61dd3cd939655c4736e81aa1807d61174085cca0/data/train/-1BAD-eeWOg_101835169_frame_000000.png
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
return get_rows(
^^^^^^^^^
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 77, in get_rows
rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2543, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2061, in __iter__
batch = formatter.format_batch(pa_table)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/formatting/formatting.py", line 472, in format_batch
batch = self.python_features_decoder.decode_batch(batch)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/formatting/formatting.py", line 234, in decode_batch
return self.features.decode_batch(batch, token_per_repo_id=self.token_per_repo_id) if self.features else batch
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 2161, in decode_batch
decode_nested_example(self[column_name], value, token_per_repo_id=token_per_repo_id)
File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1419, in decode_nested_example
return schema.decode_example(obj, token_per_repo_id=token_per_repo_id) if obj is not None else None
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/features/image.py", line 188, in decode_example
with xopen(path, "rb", download_config=download_config) as f:
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/utils/file_utils.py", line 977, in xopen
file_obj = fsspec.open(file, mode=mode, *args, **kwargs).open()
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/fsspec/core.py", line 135, in open
return self.__enter__()
^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/fsspec/core.py", line 103, in __enter__
f = self.fs.open(self.path, mode=mode)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/fsspec/spec.py", line 1293, in open
f = self._open(
^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/huggingface_hub/hf_file_system.py", line 275, in _open
return HfFileSystemFile(self, path, mode=mode, revision=revision, block_size=block_size, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/huggingface_hub/hf_file_system.py", line 947, in __init__
self.details = fs.info(self.resolved_path.unresolve(), expand_info=False)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/huggingface_hub/hf_file_system.py", line 716, in info
_raise_file_not_found(path, None)
File "/usr/local/lib/python3.12/site-packages/huggingface_hub/hf_file_system.py", line 1138, in _raise_file_not_found
raise FileNotFoundError(msg) from err
FileNotFoundError: datasets/nvidia/ffs_stereo4d@61dd3cd939655c4736e81aa1807d61174085cca0/data/train/-1BAD-eeWOg_101835169_frame_000000.pngNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
FFS Stereo4D
Disparity maps for stereo matching, generated from the Stereo4D dataset using FoundationStereo.
Dataset Structure
data/train/
metadata.csv
0000000.zip (first 50,000 images)
0000001.zip (next 50,000 images)
...
0000025.zip
Each zip contains disparity PNG files named {vid_id}_frame_{frame_idx:06d}.png.
- Disparity images: 3-channel uint8 784×784 PNG files encoding per-pixel disparity. Decode with:
disp = (R * 255*255 + G * 255 + B) / 1000.0. See also: https://github.com/NVlabs/FoundationStereo/blob/master/scripts/vis_dataset.py - metadata.csv: Links each disparity image back to its source YouTube video, with a
zip_filecolumn indicating which zip contains the image.
Metadata Columns
| Column | Description |
|---|---|
file_name |
Disparity image filename (inside the zip) |
zip_file |
Which zip file contains this image |
vid_id |
Clip identifier (matches the .npz calibration file) |
frame_idx |
Frame index in the rectified stereo output |
youtube_video_id |
YouTube video ID of the source 360 video |
timestamp_us |
Timestamp in microseconds in the original video |
timestamp_sec |
Timestamp in seconds |
video_frame_index |
Estimated frame number in the original video |
fps |
FPS of the source video |
Retrieving Source RGB Frames
This dataset contains disparity maps only. Due to the copyrights of these videos, users need to download on your own behalf. The corresponding left/right RGB stereo pairs can be recovered by:
- Following stereo4d toolkit to download the YouTube video using
youtube_video_id. - Seek to
timestamp_sec(orvideo_frame_index) to locate the source frame. - Apply equirectangular rectification using the Stereo4D calibration
.npzfiles to obtain the left and right perspective images.
Generation Pipeline
- Source: YouTube 360 videos from the Stereo4D dataset.
- Rectification: Equirectangular frames are rectified and cropped to 1024×1024 perspective stereo pairs.
- Disparity estimation: FoundationStereo computes dense disparity at 784×784 resolution (resized by
scale=0.765625of the 1024×1024 input).
Camera Parameters
The rectified stereo pairs are generated at 1024×1024 with the following pinhole camera model:
| Parameter | Value (1024×1024 rectified) | Value (784×784 disparity) | Formula |
|---|---|---|---|
| HFOV | 60° | 60° | output_hfov in batch_rectify.py |
| Baseline | 0.063 m | 0.063 m | Assumed interpupillary distance for VR180 cameras |
| fx, fy | 886.8 px | 678.8 px | size * 0.5 / tan(0.5 * HFOV * pi/180) |
| cx, cy | 512 px | 392 px | Image center |
Depth is derived as: depth = fx * baseline / disparity.
Since disparity is computed at 784×784 resolution (scale factor 784/1024 = 0.765625 of the 1024×1024 input), use the 784×784 camera parameters when converting disparity to depth:
import numpy as np
hfov = 60 # degrees
baseline = 0.063 # meters
imw = 784
fx = imw * 0.5 / np.tan(0.5 * np.radians(hfov)) # 678.8 px
depth = fx * baseline / disparity
Citation
If you use this dataset, please consider cite:
@article{wen2026fastfoundationstereo,
title={Fast-FoundationStereo: Real-Time Zero-Shot Stereo Matching},
author={Bowen Wen and Shaurya Dewan and Stan Birchfield},
journal={CVPR},
year={2026}
}
@article{wen2025foundationstereo,
title={FoundationStereo: Zero-Shot Stereo Matching},
author={Wen, Bowen and Trepte, Matthew and Aribido, Joseph and Kautz, Jan and Birchfield, Stan and Wan, Yao},
journal={CVPR},
year={2025}
}
@inproceedings{jin2025stereo4d,
title={{Stereo4D: Learning How Things Move in 3D from Internet Stereo Videos}},
author={Jin, Linyi and Tucker, Richard and Li, Zhengqi and Fouhey, David and Snavely, Noah and Holynski, Aleksander},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year={2025},
}
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