A Survey on 3D Gaussian Splatting Applications: Segmentation, Editing, and Generation

[2508.09977] A Survey on 3D Gaussian Splatting Applications: Segmentation, Editing, and Generation 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:2508.09977 (cs) [Submitted on 13 Aug 2025 ( v1 ), last revised 15 Jun 2026 (this version, v5)] Title: A Survey on 3D Gaussian Splatting Applications: Segmentation, Editing, and Generation Authors: Shuting He , Peilin Ji , Yitong Yang , Changshuo Wang , Jiayi Ji , Yinglin Wang , Henghui Ding View a PDF of the paper titled A Survey on 3D Gaussian Splatting Applications: Segmentation, Editing, and Generation, by Shuting He and 6 other authors View PDF HTML (experimental) Abstract: In the context of novel view synthesis, 3D Gaussian Splatting (3DGS) has recently emerged as an efficient and competitive counterpart to Neural Radiance Field (NeRF), enabling high-fidelity photorealistic rendering in real time. Beyond novel view synthesis, the explicit and compact nature of 3DGS enables a wide range of downstream applications that require geometric and semantic understanding. This survey provides a comprehensive overview of recent progress in 3DGS applications. It first reviews the reconstruction preliminaries of 3DGS, followed by the problem formulation, 2D foundation models, and related NeRF-based research areas that inform downstream 3DGS applications. We then categorize 3DGS applications into three foundational tasks: segmentation, editing, and generation, alongside additional functional applications built upon or tightly coupled with these foundational capabilities. For each, we summarize representative methods, supervision strategies, and learning paradigms, highlighting shared design principles and emerging trends. Commonly used datasets and evaluation protocols are also summarized, along with comparative analyses of recent methods across public benchmarks. 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Comments: IEEE TPAMI, GitHub Repo: this https URL Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2508.09977 [cs.CV] (or arXiv:2508.09977v5 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2508.09977 Focus to learn more arXiv-issued DOI via DataCite Journal reference: IEEE Transactions on Pattern Analysis and Machine Intelligence, 2026 Submission history From: Shuting He [ view email ] [v1] Wed, 13 Aug 2025 17:44:39 UTC (4,102 KB) [v2] Fri, 22 Aug 2025 13:22:47 UTC (4,136 KB) [v3] Thu, 1 Jan 2026 07:17:04 UTC (6,473 KB) [v4] Sat, 11 Apr 2026 08:26:14 UTC (6,532 KB) [v5] Mon, 15 Jun 2026 08:44:23 UTC (6,532 KB) Full-text links: Access Paper: View a PDF of the paper titled A Survey on 3D Gaussian Splatting Applications: Segmentation, Editing, and Generation, by Shuting He and 6 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 References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... 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