GOFish: Full Field-of-View Gaussian Splatting and Mesh Extraction
Paper • 2610.37818 • 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: CastError
Message: Couldn't cast
dataset: string
scene: string
gaussians: struct<source_checkpoint: string, source_sha256: string, global_step: int64, num_gaussians: int64, s (... 332 chars omitted)
child 0, source_checkpoint: string
child 1, source_sha256: string
child 2, global_step: int64
child 3, num_gaussians: int64
child 4, sh_degree: int64
child 5, feature_type: string
child 6, filter_note: list<item: string>
child 0, item: string
child 7, mip3d_filter_schema: string
child 8, mip3d_algorithm: string
child 9, verify: struct<positions_max_abs: double, scale_eff_max_rel: double, opacity_max_abs: double, rotation_max_a (... 59 chars omitted)
child 0, positions_max_abs: double
child 1, scale_eff_max_rel: double
child 2, opacity_max_abs: double
child 3, rotation_max_abs: double
child 4, sh_dc_max_abs: double
child 5, sh_rest_max_abs: double
child 10, ply_sha256: string
child 11, ply_bytes: int64
gaussians_geo: struct<source_checkpoint: string, source_sha256: string, global_step: int64, num_gaussians: int64, s (... 332 chars omitted)
child 0, source_checkpoint: string
child 1, source_sha256: string
child 2, global_step: int64
child 3, num_gaussians: int64
child 4, sh_degree: int64
child 5, feature_type: string
child 6, filter_note: list<item: string>
child 0, item: string
child 7, mip3d_filter_schema: string
child 8, mip3d_algorithm: string
child 9, verify: struct<positions_max_abs: double, scale_eff_max_rel: double, opacity_max_abs: double, rotation_max_a (... 59 chars omitted)
child 0, positions_max_abs: double
child 1, scale_eff_max_rel: double
child 2, opacity_max_abs: double
child 3, rotation_max_abs: double
child 4, sh_dc_max_abs: double
child 5, sh_rest_max_abs: double
child 10, ply_sha256: string
child 11, ply_bytes: int64
mesh: struct<source: string, source_sha256: string, sha256: string, bytes: int64>
child 0, source: string
child 1, source_sha256: string
child 2, sha256: string
child 3, bytes: int64
n_frames: struct<train: int64, test: int64>
child 0, train: int64
child 1, test: int64
files: list<item: struct<path: string, bytes: int64, sha256: string>>
child 0, item: struct<path: string, bytes: int64, sha256: string>
child 0, path: string
child 1, bytes: int64
child 2, sha256: string
to
{'scene': Value('string'), 'files': List({'path': Value('string'), 'bytes': Value('int64'), 'sha256': Value('string')})}
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
dataset: string
scene: string
gaussians: struct<source_checkpoint: string, source_sha256: string, global_step: int64, num_gaussians: int64, s (... 332 chars omitted)
child 0, source_checkpoint: string
child 1, source_sha256: string
child 2, global_step: int64
child 3, num_gaussians: int64
child 4, sh_degree: int64
child 5, feature_type: string
child 6, filter_note: list<item: string>
child 0, item: string
child 7, mip3d_filter_schema: string
child 8, mip3d_algorithm: string
child 9, verify: struct<positions_max_abs: double, scale_eff_max_rel: double, opacity_max_abs: double, rotation_max_a (... 59 chars omitted)
child 0, positions_max_abs: double
child 1, scale_eff_max_rel: double
child 2, opacity_max_abs: double
child 3, rotation_max_abs: double
child 4, sh_dc_max_abs: double
child 5, sh_rest_max_abs: double
child 10, ply_sha256: string
child 11, ply_bytes: int64
gaussians_geo: struct<source_checkpoint: string, source_sha256: string, global_step: int64, num_gaussians: int64, s (... 332 chars omitted)
child 0, source_checkpoint: string
child 1, source_sha256: string
child 2, global_step: int64
child 3, num_gaussians: int64
child 4, sh_degree: int64
child 5, feature_type: string
child 6, filter_note: list<item: string>
child 0, item: string
child 7, mip3d_filter_schema: string
child 8, mip3d_algorithm: string
child 9, verify: struct<positions_max_abs: double, scale_eff_max_rel: double, opacity_max_abs: double, rotation_max_a (... 59 chars omitted)
child 0, positions_max_abs: double
child 1, scale_eff_max_rel: double
child 2, opacity_max_abs: double
child 3, rotation_max_abs: double
child 4, sh_dc_max_abs: double
child 5, sh_rest_max_abs: double
child 10, ply_sha256: string
child 11, ply_bytes: int64
mesh: struct<source: string, source_sha256: string, sha256: string, bytes: int64>
child 0, source: string
child 1, source_sha256: string
child 2, sha256: string
child 3, bytes: int64
n_frames: struct<train: int64, test: int64>
child 0, train: int64
child 1, test: int64
files: list<item: struct<path: string, bytes: int64, sha256: string>>
child 0, item: struct<path: string, bytes: int64, sha256: string>
child 0, path: string
child 1, bytes: int64
child 2, sha256: string
to
{'scene': Value('string'), 'files': List({'path': Value('string'), 'bytes': Value('int64'), 'sha256': Value('string')})}
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.
GOFish: Full Field-of-View Gaussian Splatting and Mesh Extraction
Allen Tu1,2, Meher Gitika Karumuri1, Shifan Zhu1,3, Hailing Wang1,4, Yen-Liang Lin1, David Held1,5, Matthias Zwicker2, Tom Goldstein2
1Amazon, 2University of Maryland, College Park, 3University of Massachusetts Amherst, 4Northeastern University, 5Carnegie Mellon University
Project page | Paper | Code
Scenes: FIORD and FullCircle
Each folder <Dataset>_<Scene>/ holds one scene reconstructed over the full fisheye field of view
(FIORD 200 degrees, FullCircle about 205 degrees per lens), as evaluated in the GOFish paper.
| Folder | Contents |
|---|---|
gaussians/ |
GOFish (Ours): the Gaussian Splatting scene trained without SOF's geometry losses. Used for all rendering results. |
gaussians_geo/ |
GOFish + Geometry Objective: the same pipeline with SOF's geometry losses. |
mesh/ |
The + Geometry Objective mesh used in the paper's mesh results, as binary PLY and Draco-compressed GLB. |
cameras.json |
Calibration (OpenCV fisheye model) and every train/test camera pose. |
lidar_alignment.json |
FIORD geometry scenes only: the similarity transform into the LiDAR ground-truth frame used for the paper's mesh scores. Corridor_Out has none because its SfM-LiDAR registration is invalid, so the paper evaluates it for rendering only. |
Download
Download one scene, or omit --include for all of them:
hf download AllenTu/GOFish-scenes --repo-type dataset --include "FIORD_Kitchen_In/*" --local-dir GOFish-scenes
Formats
gaussians*/point_cloud.ply: standard 3D Gaussian Splatting (INRIA) layout:x y z nx ny nz f_dc_0..2 f_rest_0..44 opacity scale_0..2 rot_0..3. Opacity is a logit (apply sigmoid), scales are log (apply exp), rotation is a quaternion w,x,y,z, colors are degree-3 spherical harmonics with channel-majorf_rest. The GOFish 3-D Mip filter is baked in: each stored scale issqrt(scale^2 + filter_3D^2), so the file is exactly the stored scene GOFish renders and meshes. Opacity is not rescaled, matching GOFish.gaussians*/appearance.json: the PPISP sensor model and GOFish's wide-angle attenuation W. This covers per-image exposure and color, per-camera vignetting, the camera response function, the radial profile and flat field, and the novel-view controller weights. Tensors are flattened row-major with their shapes. Generic viewers ignore this file, so they show the scene's radiance without the camera's exposure and lens falloff.mesh/mesh.ply: the mesh exactly as evaluated, byte-identical to the paper's file.mesh/mesh.glbis the same mesh with Draco compression (KHR_draco_mesh_compression, 16-bit positions) for web viewers.cameras.json: COLMAP convention,x_cam = R(q) x_world + t, withcamera_to_worldgiven as its inverse. Intrinsics are at the stored image resolution. GOFish trained on images downsampled by 4, so divide fx, fy, cx and cy by 4 for the training resolution. Gaussians, mesh and cameras share one world frame, the training COLMAP registration, with no normalization.
Viewing
- Gaussians: SuperSplat (https://superspl.at/editor), gsplat, Nerfstudio or any 3DGS PLY viewer. These render with a pinhole projection. GOFish's full-FoV fisheye rendering needs the GOFish/3DGUT renderer with
cameras.jsonandappearance.json. - Mesh: MeshLab, Blender or Open3D for
mesh.ply; three.js, Babylon.js or a glTF viewer with Draco support formesh.glb.
MANIFEST.json in each folder lists every file with its size and sha256, and each scene's README.md gives its counts and provenance.
Citation
If you use these scenes, please cite GOFish and the source dataset.
@misc{tu2026gofish,
title = {{GOFish}: Full Field-of-View {Gaussian} {Splatting} and Mesh Extraction},
author = {Tu, Allen and Karumuri, Meher Gitika and Zhu, Shifan and Wang, Hailing and Lin, Yen-Liang and Held, David and Zwicker, Matthias and Goldstein, Tom},
year = {2026},
eprint = {2610.37818},
archivePrefix = {arXiv},
primaryClass = {cs.CV}
}
@inproceedings{gunes2025fiord,
author = {Gunes, Ulas and Turkulainen, Matias and Ren, Xuqian and Solin, Arno and Kannala, Juho and Rahtu, Esa},
title = {{FIORD}: A Fisheye Indoor-Outdoor Dataset with {LIDAR} Ground Truth for {3D} Scene Reconstruction and Benchmarking},
booktitle = {Proceedings of the Scandinavian Conference on Image Analysis (SCIA)},
pages = {3--17},
year = {2025}
}
@article{foroutan2026fullcircle,
author = {Foroutan, Yalda and Oztas, Ipek and Rebain, Daniel and Dundar, Aysegul and Yi, Kwang Moo and Goli, Lily and Tagliasacchi, Andrea},
title = {{FullCircle}: Effortless {3D} Reconstruction from Casual 360$^\circ$ Captures},
journal = {arXiv preprint arXiv:2603.22572},
year = {2026}
}
License
These reconstructions are released under CC BY-NC 4.0, matching the GOFish code release. They are derived from the following datasets, whose licenses and attribution terms also apply:
- FIORD (Gunes et al., SCIA 2025), Zenodo, CC BY 4.0.
- FullCircle (Foroutan et al., 2026), Hugging Face, Apache 2.0.
Acknowledgements
This work was completed during an internship at Amazon. We thank Ziyang Long, Mellon M. Zhang, Chenqi Li, Sarosij Bose, Srikumar Sastry, Amin Kheradmand, David Stafford, Yongqing Ye, Huixian Gong, Yun He, Zeping Liu, Haiyang Ying, Alex Hanson, Chengyue Huang, and Chen Xu for helpful discussions and feedback on this work.
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