Datasets:

AllenTu
/

GOFish-scenes

license: cc-by-nc-4.0
pretty_name: GOFish scenes
size_categories:
  - n<1K
task_categories:
  - image-to-3d
tags:
  - gaussian-splatting
  - 3d-reconstruction
  - mesh
  - fisheye

GOFish: Full Field-of-View Gaussian Splatting and Mesh Extraction

Allen Tu^1,2, Meher Gitika Karumuri¹, Shifan Zhu^1,3, Hailing Wang^1,4, Yen-Liang Lin¹, David Held^1,5, 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-major f_rest. The GOFish 3-D Mip filter is baked in: each stored scale is sqrt(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.glb is 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, with camera_to_world given 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.json and appearance.json.
Mesh: MeshLab, Blender or Open3D for mesh.ply; three.js, Babylon.js or a glTF viewer with Draco support for mesh.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,
    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},
    title         = {{GOFish}: Full Field-of-View Gaussian Splatting and Mesh Extraction},
    year          = {2026},
    eprint        = {2610.37818},
    archivePrefix = {arXiv},
    primaryClass  = {cs.CV},
    url           = {https://3dgofish.github.io/}
}
@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.