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: CastError
Message: Couldn't cast
building: struct<generated_points3d_ply: bool, point_count: int64, status: string, test: int64, train: int64, (... 37 chars omitted)
child 0, generated_points3d_ply: bool
child 1, point_count: int64
child 2, status: string
child 3, test: int64
child 4, train: int64
child 5, train_sharp_via_video_cameras: int64
cozyroom: struct<generated_points3d_ply: bool, point_count: int64, status: string, test: int64, train: int64, (... 37 chars omitted)
child 0, generated_points3d_ply: bool
child 1, point_count: int64
child 2, status: string
child 3, test: int64
child 4, train: int64
child 5, train_sharp_via_video_cameras: int64
factory: struct<generated_points3d_ply: bool, point_count: int64, status: string, test: int64, train: int64, (... 37 chars omitted)
child 0, generated_points3d_ply: bool
child 1, point_count: int64
child 2, status: string
child 3, test: int64
child 4, train: int64
child 5, train_sharp_via_video_cameras: int64
livingroom: struct<generated_points3d_ply: bool, point_count: int64, status: string, test: int64, train: int64, (... 37 chars omitted)
child 0, generated_points3d_ply: bool
child 1, point_count: int64
child 2, status: string
child 3, test: int64
child 4, train: int64
child 5, train_sharp_via_video_cameras: int64
pool: struct<generated_points3d_ply: bool, point_count: int64, status: string, test: int64, train: int64, (... 37 chars omitted)
child 0, generated_points3d_ply: bool
child 1, point_count: int64
...
rms_test.json: int64, transforms_train.json: int64, transforms_train_sharp.json: int64 (... 1 chars omitted)
child 0, transforms_test.json: int64
child 1, transforms_train.json: int64
child 2, transforms_train_sharp.json: int64
child 6, tanabata: struct<archive: string, archive_bytes: int64, archive_sha256: string, colmap: struct<cameras: int64, (... 429 chars omitted)
child 0, archive: string
child 1, archive_bytes: int64
child 2, archive_sha256: string
child 3, colmap: struct<cameras: int64, points3D: int64, registered_images: int64>
child 0, cameras: int64
child 1, points3D: int64
child 2, registered_images: int64
child 4, derived_fovy_finite: bool
child 5, focal_length_mm: struct<max: double, min: double>
child 0, max: double
child 1, min: double
child 6, image_dimensions_by_frame_reference: struct<600x400: int64>
child 0, 600x400: int64
child 7, json_references_after_extraction: bool
child 8, loader_preflight: string
child 9, manifest_entries_verified: int64
child 10, safe_members_no_links: bool
child 11, split_counts: struct<transforms_test.json: int64, transforms_train.json: int64, transforms_train_sharp.json: int64 (... 1 chars omitted)
child 0, transforms_test.json: int64
child 1, transforms_train.json: int64
child 2, transforms_train_sharp.json: int64
all_archives_valid: bool
to
{'all_archives_valid': Value('bool'), 'scenes': {'building': {'archive': Value('string'), 'archive_bytes': Value('int64'), 'archive_sha256': Value('string'), 'colmap': {'cameras': Value('int64'), 'points3D': Value('int64'), 'registered_images': Value('int64')}, 'derived_fovy_finite': Value('bool'), 'focal_length_mm': {'max': Value('float64'), 'min': Value('float64')}, 'image_dimensions_by_frame_reference': {'800x340': Value('int64')}, 'json_references_after_extraction': Value('bool'), 'loader_preflight': Value('string'), 'manifest_entries_verified': Value('int64'), 'safe_members_no_links': Value('bool'), 'split_counts': {'transforms_test.json': Value('int64'), 'transforms_train.json': Value('int64'), 'transforms_train_sharp.json': Value('int64')}}, 'cozyroom': {'archive': Value('string'), 'archive_bytes': Value('int64'), 'archive_sha256': Value('string'), 'colmap': {'cameras': Value('int64'), 'points3D': Value('int64'), 'registered_images': Value('int64')}, 'derived_fovy_finite': Value('bool'), 'focal_length_mm': {'max': Value('float64'), 'min': Value('float64')}, 'image_dimensions_by_frame_reference': {'600x400': Value('int64')}, 'json_references_after_extraction': Value('bool'), 'loader_preflight': Value('string'), 'manifest_entries_verified': Value('int64'), 'safe_members_no_links': Value('bool'), 'split_counts': {'transforms_test.json': Value('int64'), 'transforms_train.json': Value('int64'), 'transforms_train_sharp.json': Value('int64')}}, 'factory': {'archive': Value('s
...
nt64'), 'transforms_train_sharp.json': Value('int64')}}, 'ship': {'archive': Value('string'), 'archive_bytes': Value('int64'), 'archive_sha256': Value('string'), 'colmap': {'cameras': Value('int64'), 'points3D': Value('int64'), 'registered_images': Value('int64')}, 'derived_fovy_finite': Value('bool'), 'focal_length_mm': {'max': Value('float64'), 'min': Value('float64')}, 'image_dimensions_by_frame_reference': {'800x384': Value('int64'), '800x395': Value('int64')}, 'json_references_after_extraction': Value('bool'), 'loader_preflight': Value('string'), 'manifest_entries_verified': Value('int64'), 'safe_members_no_links': Value('bool'), 'split_counts': {'transforms_test.json': Value('int64'), 'transforms_train.json': Value('int64'), 'transforms_train_sharp.json': Value('int64')}}, 'tanabata': {'archive': Value('string'), 'archive_bytes': Value('int64'), 'archive_sha256': Value('string'), 'colmap': {'cameras': Value('int64'), 'points3D': Value('int64'), 'registered_images': Value('int64')}, 'derived_fovy_finite': Value('bool'), 'focal_length_mm': {'max': Value('float64'), 'min': Value('float64')}, 'image_dimensions_by_frame_reference': {'600x400': Value('int64')}, 'json_references_after_extraction': Value('bool'), 'loader_preflight': Value('string'), 'manifest_entries_verified': Value('int64'), 'safe_members_no_links': Value('bool'), 'split_counts': {'transforms_test.json': Value('int64'), 'transforms_train.json': Value('int64'), 'transforms_train_sharp.json': Value('int64')}}}}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 149, 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 129, 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 544, 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 3052, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2559, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2584, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 558, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 440, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 361, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2381, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2309, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
building: struct<generated_points3d_ply: bool, point_count: int64, status: string, test: int64, train: int64, (... 37 chars omitted)
child 0, generated_points3d_ply: bool
child 1, point_count: int64
child 2, status: string
child 3, test: int64
child 4, train: int64
child 5, train_sharp_via_video_cameras: int64
cozyroom: struct<generated_points3d_ply: bool, point_count: int64, status: string, test: int64, train: int64, (... 37 chars omitted)
child 0, generated_points3d_ply: bool
child 1, point_count: int64
child 2, status: string
child 3, test: int64
child 4, train: int64
child 5, train_sharp_via_video_cameras: int64
factory: struct<generated_points3d_ply: bool, point_count: int64, status: string, test: int64, train: int64, (... 37 chars omitted)
child 0, generated_points3d_ply: bool
child 1, point_count: int64
child 2, status: string
child 3, test: int64
child 4, train: int64
child 5, train_sharp_via_video_cameras: int64
livingroom: struct<generated_points3d_ply: bool, point_count: int64, status: string, test: int64, train: int64, (... 37 chars omitted)
child 0, generated_points3d_ply: bool
child 1, point_count: int64
child 2, status: string
child 3, test: int64
child 4, train: int64
child 5, train_sharp_via_video_cameras: int64
pool: struct<generated_points3d_ply: bool, point_count: int64, status: string, test: int64, train: int64, (... 37 chars omitted)
child 0, generated_points3d_ply: bool
child 1, point_count: int64
...
rms_test.json: int64, transforms_train.json: int64, transforms_train_sharp.json: int64 (... 1 chars omitted)
child 0, transforms_test.json: int64
child 1, transforms_train.json: int64
child 2, transforms_train_sharp.json: int64
child 6, tanabata: struct<archive: string, archive_bytes: int64, archive_sha256: string, colmap: struct<cameras: int64, (... 429 chars omitted)
child 0, archive: string
child 1, archive_bytes: int64
child 2, archive_sha256: string
child 3, colmap: struct<cameras: int64, points3D: int64, registered_images: int64>
child 0, cameras: int64
child 1, points3D: int64
child 2, registered_images: int64
child 4, derived_fovy_finite: bool
child 5, focal_length_mm: struct<max: double, min: double>
child 0, max: double
child 1, min: double
child 6, image_dimensions_by_frame_reference: struct<600x400: int64>
child 0, 600x400: int64
child 7, json_references_after_extraction: bool
child 8, loader_preflight: string
child 9, manifest_entries_verified: int64
child 10, safe_members_no_links: bool
child 11, split_counts: struct<transforms_test.json: int64, transforms_train.json: int64, transforms_train_sharp.json: int64 (... 1 chars omitted)
child 0, transforms_test.json: int64
child 1, transforms_train.json: int64
child 2, transforms_train_sharp.json: int64
all_archives_valid: bool
to
{'all_archives_valid': Value('bool'), 'scenes': {'building': {'archive': Value('string'), 'archive_bytes': Value('int64'), 'archive_sha256': Value('string'), 'colmap': {'cameras': Value('int64'), 'points3D': Value('int64'), 'registered_images': Value('int64')}, 'derived_fovy_finite': Value('bool'), 'focal_length_mm': {'max': Value('float64'), 'min': Value('float64')}, 'image_dimensions_by_frame_reference': {'800x340': Value('int64')}, 'json_references_after_extraction': Value('bool'), 'loader_preflight': Value('string'), 'manifest_entries_verified': Value('int64'), 'safe_members_no_links': Value('bool'), 'split_counts': {'transforms_test.json': Value('int64'), 'transforms_train.json': Value('int64'), 'transforms_train_sharp.json': Value('int64')}}, 'cozyroom': {'archive': Value('string'), 'archive_bytes': Value('int64'), 'archive_sha256': Value('string'), 'colmap': {'cameras': Value('int64'), 'points3D': Value('int64'), 'registered_images': Value('int64')}, 'derived_fovy_finite': Value('bool'), 'focal_length_mm': {'max': Value('float64'), 'min': Value('float64')}, 'image_dimensions_by_frame_reference': {'600x400': Value('int64')}, 'json_references_after_extraction': Value('bool'), 'loader_preflight': Value('string'), 'manifest_entries_verified': Value('int64'), 'safe_members_no_links': Value('bool'), 'split_counts': {'transforms_test.json': Value('int64'), 'transforms_train.json': Value('int64'), 'transforms_train_sharp.json': Value('int64')}}, 'factory': {'archive': Value('s
...
nt64'), 'transforms_train_sharp.json': Value('int64')}}, 'ship': {'archive': Value('string'), 'archive_bytes': Value('int64'), 'archive_sha256': Value('string'), 'colmap': {'cameras': Value('int64'), 'points3D': Value('int64'), 'registered_images': Value('int64')}, 'derived_fovy_finite': Value('bool'), 'focal_length_mm': {'max': Value('float64'), 'min': Value('float64')}, 'image_dimensions_by_frame_reference': {'800x384': Value('int64'), '800x395': Value('int64')}, 'json_references_after_extraction': Value('bool'), 'loader_preflight': Value('string'), 'manifest_entries_verified': Value('int64'), 'safe_members_no_links': Value('bool'), 'split_counts': {'transforms_test.json': Value('int64'), 'transforms_train.json': Value('int64'), 'transforms_train_sharp.json': Value('int64')}}, 'tanabata': {'archive': Value('string'), 'archive_bytes': Value('int64'), 'archive_sha256': Value('string'), 'colmap': {'cameras': Value('int64'), 'points3D': Value('int64'), 'registered_images': Value('int64')}, 'derived_fovy_finite': Value('bool'), 'focal_length_mm': {'max': Value('float64'), 'min': Value('float64')}, 'image_dimensions_by_frame_reference': {'600x400': Value('int64')}, 'json_references_after_extraction': Value('bool'), 'loader_preflight': Value('string'), 'manifest_entries_verified': Value('int64'), 'safe_members_no_links': Value('bool'), 'split_counts': {'transforms_test.json': Value('int64'), 'transforms_train.json': Value('int64'), 'transforms_train_sharp.json': Value('int64')}}}}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
FocalGS Dataset
Data for FocalGS: Adaptive Focal 3D Gaussian Splatting for Continuous Zoom Rendering (SIGGRAPH Asia 2026 Conference Papers, DOI: 10.1145/3829340.3842186).
Code: https://github.com/lzy021205/FocalGS
Downloads
The repository keeps each frozen archive as an independent file:
synthetic/{scene}/: seven synthetic scenes, one archive per scene.real/experimental_archive/: Toy, Plant, and Bear data corresponding to the recoverable pre-paper experiment records.real/postpaper_clean_benchmark/: optional later Plant/Bear strict 24--85 mm benchmark. It was not used as the paper's original real-data protocol.
See DATASET.md and REAL_DATASET.md for structure, splits, camera fields, and commands. Verify downloads with:
python verify_downloads.py /path/to/downloaded/repository
Protocol notice
The recoverable real experiment archive does not exactly match the paper prose describing 15 viewpoints, five focal lengths, and two held-out viewpoints. Its actual registered-image and split counts are preserved and documented; images and splits were not changed to imitate that description. The post-paper clean benchmark remains a separate download.
License and source notice
No repository-wide data license is asserted here. The MIT license of the FocalGS or Deblur-NeRF software does not automatically license Blender assets, rendered images, real photographs, or camera data. Read SYNTHETIC_DATA_PROVENANCE.md for the evidence and unresolved asset-license chain of every synthetic scene. Users remain responsible for following applicable asset and data terms.
Citation
@inproceedings{liu2026focalgs,
title = {FocalGS: Adaptive Focal 3D Gaussian Splatting for Continuous Zoom Rendering},
author = {Liu, Zeyu and Wang, Chao and Wu, Jiahao and Liang, Jie and Wang, Ronggang},
booktitle = {SIGGRAPH Asia 2026 Conference Papers},
year = {2026},
doi = {10.1145/3829340.3842186}
}
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