A Survey on 3D Gaussian Splatting

[2401.03890] A Survey on 3D Gaussian Splatting 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:2401.03890 (cs) [Submitted on 8 Jan 2024 ( v1 ), last revised 9 Apr 2026 (this version, v9)] Title: A Survey on 3D Gaussian Splatting Authors: Guikun Chen , Wenguan Wang View a PDF of the paper titled A Survey on 3D Gaussian Splatting, by Guikun Chen and 1 other authors View PDF HTML (experimental) Abstract: 3D Gaussian splatting (GS) has emerged as a transformative technique in radiance fields. Unlike mainstream implicit neural models, 3D GS uses millions of learnable 3D Gaussians for an explicit scene representation. Paired with a differentiable rendering algorithm, this approach achieves real-time rendering and unprecedented editability, making it a potential game-changer for 3D reconstruction and representation. In the present paper, we provide the first systematic overview of the recent developments and critical contributions in 3D GS. We begin with a detailed exploration of the underlying principles and the driving forces behind the emergence of 3D GS, laying the groundwork for understanding its significance. A focal point of our discussion is the practical applicability of 3D GS. By enabling unprecedented rendering speed, 3D GS opens up a plethora of applications, ranging from virtual reality to interactive media and beyond. This is complemented by a comparative analysis of leading 3D GS models, evaluated across various benchmark tasks to highlight their performance and practical utility. The survey concludes by identifying current challenges and suggesting potential avenues for future research. Through this survey, we aim to provide a valuable resource for both newcomers and seasoned researchers, fostering further exploration and advancement in explicit radiance field. Comments: Accepted by ACM Computing Surveys; Paper list: this https URL ; Benchmark: this https URL Subjects: Computer Vision and Pattern Recognition (cs.CV) ; Artificial Intelligence (cs.AI); Graphics (cs.GR); Multimedia (cs.MM) Cite as: arXiv:2401.03890 [cs.CV] (or arXiv:2401.03890v9 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2401.03890 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Guikun Chen [ view email ] [v1] Mon, 8 Jan 2024 13:42:59 UTC (237 KB) [v2] Sun, 14 Apr 2024 06:50:24 UTC (726 KB) [v3] Sun, 7 Jul 2024 11:18:33 UTC (2,544 KB) [v4] Mon, 22 Jul 2024 05:13:49 UTC (2,544 KB) [v5] Sat, 22 Feb 2025 07:56:50 UTC (2,017 KB) [v6] Fri, 7 Mar 2025 13:06:56 UTC (2,017 KB) [v7] Fri, 22 Aug 2025 11:42:06 UTC (2,016 KB) [v8] Mon, 6 Oct 2025 15:38:40 UTC (1,580 KB) [v9] Thu, 9 Apr 2026 05:33:48 UTC (1,570 KB) Full-text links: Access Paper: View a PDF of the paper titled A Survey on 3D Gaussian Splatting, by Guikun Chen and 1 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CV < prev | next > new | recent | 2024-01 Change to browse by: cs cs.AI cs.GR cs.MM References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... 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