3D Gaussian Splatting with Fisheye Images: Field of View Analysis and Depth-Based Initialization

[2508.06968] 3D Gaussian Splatting with Fisheye Images: Field of View Analysis and Depth-Based Initialization Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computer Vision and Pattern Recognition arXiv:2508.06968 (cs) [Submitted on 9 Aug 2025 ( v1 ), last revised 7 Mar 2026 (this version, v2)] Title: 3D Gaussian Splatting with Fisheye Images: Field of View Analysis and Depth-Based Initialization Authors: Ulas Gunes , Matias Turkulainen , Mikhail Silaev , Juho Kannala , Esa Rahtu View a PDF of the paper titled 3D Gaussian Splatting with Fisheye Images: Field of View Analysis and Depth-Based Initialization, by Ulas Gunes and 4 other authors View PDF HTML (experimental) Abstract: We present the first evaluation of 3D Gaussian Splatting methods on real fisheye imagery with fields of view above 180\textdegree{}. Our study evaluates Fisheye-GS \cite{liao2024fisheyegslightweightextensiblegaussian} and 3DGUT \cite{wu20253dgut} on indoor and outdoor scenes captured with 200\textdegree{} fisheye cameras, with the aim of assessing the practicality of wide-angle reconstruction under severe distortion. By comparing reconstructions at 200\textdegree{}, 160\textdegree{}, and 120\textdegree{} field-of-view, we show that both methods achieve their best results at 160\textdegree{}, which balances scene coverage with image quality, while distortion at 200\textdegree{} degrades performance. To address the common failure of Structure-from-Motion (SfM) initialization at such wide angles, we introduce a depth-based alternative using UniK3D (Universal Camera Monocular 3D Estimation) \cite{piccinelli2025unik3d}. This represents the first application of UniK3D to fisheye imagery beyond 200\textdegree{}, despite the model not being trained on such data. With the number of predicted points controlled to match SfM for fairness, UniK3D produces geometrically accurate reconstructions that rival or surpass SfM, even in challenging scenes with fog, glare, or open sky. These results demonstrate the feasibility of fisheye-based 3D Gaussian Splatting and provides a benchmark for future research on wide-angle reconstruction from sparse and distorted inputs. Comments: VISAPP 2026 Accepted Camera Ready Version Subjects: Computer Vision and Pattern Recognition (cs.CV) ; Graphics (cs.GR) Cite as: arXiv:2508.06968 [cs.CV] (or arXiv:2508.06968v2 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2508.06968 Focus to learn more arXiv-issued DOI via DataCite Related DOI : https://doi.org/10.5220/0014031200004084 Focus to learn more DOI(s) linking to related resources Submission history From: Ulas Gunes [ view email ] [v1] Sat, 9 Aug 2025 12:29:17 UTC (26,953 KB) [v2] Sat, 7 Mar 2026 18:20:51 UTC (41,016 KB) Full-text links: Access Paper: View a PDF of the paper titled 3D Gaussian Splatting with Fisheye Images: Field of View Analysis and Depth-Based Initialization, by Ulas Gunes and 4 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CV < prev | next > new | recent | 2025-08 Change to browse by: cs cs.GR References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... 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