GOFish: Full Field-of-View Gaussian Splatting and Mesh Extraction
GOFish: Full Field-of-View Gaussian Splatting and Mesh Extraction
GOFish is a new 3D Gaussian Splatting method designed to work directly with fisheye images, enabling full field-of-view rendering and aligned mesh extraction. It outperforms prior methods in PSNR and mesh F-score on benchmark datasets, offering a LiDAR-free, simulation-ready digital twin capable of real-time rendering and full FoV ray tracing.
Source: arXiv Robotics — research abstracts · Read original article ↗
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What the source reports
Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
Reported numbers
PSNR improvement
2.28
View original evidence
scene and mesh. Across the 200{\deg} FIORD and 205{\deg} FullCircle benchmarks, GOFish improves PSNR over the best prior method by 2.28 and 1.97 dB, respectively. It also outperforms fisheye rendering baselines within their own FoVs and achieves the highest mesh F-score on FIORD and the main ScanNet++ set. Together, the Gaussian scene and mesh form a LiDAR-free, simulation-ready digital twin that renders in real tim
Open source S4PSNR improvement
1.97
View original evidence
scene and mesh. Across the 200{\deg} FIORD and 205{\deg} FullCircle benchmarks, GOFish improves PSNR over the best prior method by 2.28 and 1.97 dB, respectively. It also outperforms fisheye rendering baselines within their own FoVs and achieves the highest mesh F-score on FIORD and the main ScanNet++ set. Together, the Gaussian scene and mesh form a LiDAR-free, simulation-ready digital twin that renders in real tim
Open source S4field of view
200
View original evidence
scene and mesh. Across the 200{\deg} FIORD and 205{\deg} FullCircle benchmarks, GOFish improves PSNR over the best prior method by 2.28 and 1.97 dB, respectively. It also outperforms fisheye rendering baselines within their own FoVs and achieves the highest mesh F-score on FIORD and the main ScanNet++ set. Together, the Gaussian scene and mesh form a LiDAR-free, simulation-ready digital twin that renders in real tim
Open source S4field of view
205
View original evidence
scene and mesh. Across the 200{\deg} FIORD and 205{\deg} FullCircle benchmarks, GOFish improves PSNR over the best prior method by 2.28 and 1.97 dB, respectively. It also outperforms fisheye rendering baselines within their own FoVs and achieves the highest mesh F-score on FIORD and the main ScanNet++ set. Together, the Gaussian scene and mesh form a LiDAR-free, simulation-ready digital twin that renders in real tim
Open source S4field of view
≤180
View original evidence
arXiv:2610.37818v1 Announce Type: cross Abstract: Robots, autonomous vehicles, and consumer 360{\deg} cameras use fisheye lenses for wide fields of view (FoVs). Simulation-ready digital twins require photorealistic rendering across the full FoV and an aligned collision mesh. However, prior fisheye Gaussian Splatting methods only rasterize up to 180{\deg} and do not produce meshes, while Gaussian surface reconstructio
Open source S2field of view
180
View original evidence
n methods assume pinhole training images. We present GOFish, the first 3D Gaussian Splatting surface reconstruction method to train directly on fisheye images, rasterize FoVs beyond 180{\deg}, and extract an aligned mesh from the same scene. Calibrated densification recovers the surface across the entire FoV, while Calibrated Mip Filtering and Wide-Angle Appearance Modeling keep pixel blur and lens falloff out of the
Open source S3
- paper: not_reported
- code: not_reported
- weights: not_reported
- dataset: not_reported
Source excerpts and review record
Automatically extracted; no manual editorial approval recorded.
scene and mesh. Across the 200{\deg} FIORD and 205{\deg} FullCircle benchmarks, GOFish improves PSNR over the best prior method by 2.28 and 1.97 dB, respectively. It also outperforms fisheye rendering baselines within their own FoVs and achieves the highest mesh F-score on FIORD and the main ScanNet++ set. Together, the Gaussian scene and mesh form a LiDAR-free, simulation-ready digital twin that renders in real tim
Open source S4
arXiv:2610.37818v1 Announce Type: cross Abstract: Robots, autonomous vehicles, and consumer 360{\deg} cameras use fisheye lenses for wide fields of view (FoVs). Simulation-ready digital twins require photorealistic rendering across the full FoV and an aligned collision mesh. However, prior fisheye Gaussian Splatting methods only rasterize up to 180{\deg} and do not produce meshes, while Gaussian surface reconstructio
Open source S2
n methods assume pinhole training images. We present GOFish, the first 3D Gaussian Splatting surface reconstruction method to train directly on fisheye images, rasterize FoVs beyond 180{\deg}, and extract an aligned mesh from the same scene. Calibrated densification recovers the surface across the entire FoV, while Calibrated Mip Filtering and Wide-Angle Appearance Modeling keep pixel blur and lens falloff out of the
Open source S3
Source：arXiv Robotics — research abstracts · arxiv.org