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](https://tuallen.github.io/)<sup>1,2</sup>, [Meher Gitika Karumuri](https://scholar.google.com/citations?user=R14csHAAAAAJ)<sup>1</sup>, [Shifan Zhu](https://shifanzhu.github.io/)<sup>1,3</sup>, [Hailing Wang](https://scholar.google.com/citations?user=oKoYjqUAAAAJ)<sup>1,4</sup>, [Yen-Liang Lin](https://sites.google.com/view/yenlianglin)<sup>1</sup>, [David Held](https://davheld.github.io/)<sup>1,5</sup>, [Matthias Zwicker](https://www.cs.umd.edu/~zwicker/)<sup>2</sup>, [Tom Goldstein](https://www.cs.umd.edu/~tomg/)<sup>2</sup>

<sup>1</sup>Amazon, <sup>2</sup>University of Maryland, College Park, <sup>3</sup>University of Massachusetts Amherst, <sup>4</sup>Northeastern University, <sup>5</sup>Carnegie Mellon University

[Project page](https://3dgofish.github.io/) | [Paper](https://arxiv.org/abs/2610.37818) | [Code](https://github.com/tuallen/GOFish)

![GOFish reconstructs FIORD Hall_In as a Gaussian Splatting scene and aligned mesh over its full 200° field of view; 3DGUT + PPISP stops at 160°.](assets/teaser.jpg)

## 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.
```bibtex
@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](https://creativecommons.org/licenses/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](https://zenodo.org/communities/fiord), CC BY 4.0.
- **FullCircle** (Foroutan et al., 2026), [Hugging Face](https://huggingface.co/datasets/youlenda/FullCircle), Apache 2.0.
## Acknowledgements
This work was completed during an internship at [Amazon](https://www.amazon.science/). We thank
[Ziyang Long](https://ziyanglong.cn/), [Mellon M. Zhang](https://meilongzhang.github.io/),
[Chenqi Li](https://scholar.google.com/citations?user=u_lYTIwAAAAJ), [Sarosij Bose](https://sarosijbose.github.io/),
[Srikumar Sastry](https://vishu26.github.io/), [Amin Kheradmand](https://scholar.google.com/citations?user=5DJL154AAAAJ),
[David Stafford](https://www.linkedin.com/in/davidwstafford), [Yongqing Ye](https://www.linkedin.com/in/avatarye),
[Huixian Gong](https://www.linkedin.com/in/huixiangong), [Yun He](https://yunhe20.github.io/),
[Zeping Liu](https://zpl99.github.io/), [Haiyang Ying](https://oceanying.github.io),
[Alex Hanson](https://www.cs.umd.edu/~hanson/), [Chengyue Huang](https://chengyuehuang511.github.io/), and Chen Xu
for helpful discussions and feedback on this work.

	---
	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](https://tuallen.github.io/)<sup>1,2</sup>, [Meher Gitika Karumuri](https://scholar.google.com/citations?user=R14csHAAAAAJ)<sup>1</sup>, [Shifan Zhu](https://shifanzhu.github.io/)<sup>1,3</sup>, [Hailing Wang](https://scholar.google.com/citations?user=oKoYjqUAAAAJ)<sup>1,4</sup>, [Yen-Liang Lin](https://sites.google.com/view/yenlianglin)<sup>1</sup>, [David Held](https://davheld.github.io/)<sup>1,5</sup>, [Matthias Zwicker](https://www.cs.umd.edu/~zwicker/)<sup>2</sup>, [Tom Goldstein](https://www.cs.umd.edu/~tomg/)<sup>2</sup>

	<sup>1</sup>Amazon, <sup>2</sup>University of Maryland, College Park, <sup>3</sup>University of Massachusetts Amherst, <sup>4</sup>Northeastern University, <sup>5</sup>Carnegie Mellon University

	[Project page](https://3dgofish.github.io/) \| [Paper](https://arxiv.org/abs/2610.37818) \| [Code](https://github.com/tuallen/GOFish)

	![GOFish reconstructs FIORD Hall_In as a Gaussian Splatting scene and aligned mesh over its full 200° field of view; 3DGUT + PPISP stops at 160°.](assets/teaser.jpg)

	## 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.
	```bibtex
	@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](https://creativecommons.org/licenses/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](https://zenodo.org/communities/fiord), CC BY 4.0.
	- FullCircle (Foroutan et al., 2026), [Hugging Face](https://huggingface.co/datasets/youlenda/FullCircle), Apache 2.0.

	## Acknowledgements
	This work was completed during an internship at [Amazon](https://www.amazon.science/). We thank
	[Ziyang Long](https://ziyanglong.cn/), [Mellon M. Zhang](https://meilongzhang.github.io/),
	[Chenqi Li](https://scholar.google.com/citations?user=u_lYTIwAAAAJ), [Sarosij Bose](https://sarosijbose.github.io/),
	[Srikumar Sastry](https://vishu26.github.io/), [Amin Kheradmand](https://scholar.google.com/citations?user=5DJL154AAAAJ),
	[David Stafford](https://www.linkedin.com/in/davidwstafford), [Yongqing Ye](https://www.linkedin.com/in/avatarye),
	[Huixian Gong](https://www.linkedin.com/in/huixiangong), [Yun He](https://yunhe20.github.io/),
	[Zeping Liu](https://zpl99.github.io/), [Haiyang Ying](https://oceanying.github.io),
	[Alex Hanson](https://www.cs.umd.edu/~hanson/), [Chengyue Huang](https://chengyuehuang511.github.io/), and Chen Xu
	for helpful discussions and feedback on this work.