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

• URL: http://arxiv.org/abs/2508.09977v2
• Date: Fri, 22 Aug 2025 13:22:47 GMT
• 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,
• Abstract summary: 3D Gaussian Splatting (3DGS) has emerged as a powerful alternative to Neural Radiance Fields (NeRF) for 3D scene representation.<n>This survey provides a comprehensive overview of recent progress in 3DGS applications.
• Score: 66.62489208150681
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 3D Gaussian Splatting (3DGS) has recently emerged as a powerful alternative to Neural Radiance Fields (NeRF) for 3D scene representation, offering high-fidelity photorealistic rendering with real-time performance. 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 introduces 2D foundation models that support semantic understanding and control in 3DGS applications, followed by a review of NeRF-based methods that inform their 3DGS counterparts. We then categorize 3DGS applications into segmentation, editing, generation, and other functional tasks. 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. To support ongoing research and development, a continually updated repository of papers, code, and resources is maintained at https://github.com/heshuting555/Awesome-3DGS-Applications.

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