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. 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.

翻译：在新视角合成领域，3D高斯溅射（3DGS）近期已作为神经辐射场（NeRF）的高效且具竞争力的替代方案崭露头角，能够实现实时的高保真逼真渲染。除新视角合成外，3DGS的显式与紧凑特性还使其适用于需要几何与语义理解的广泛下游应用。本综述系统梳理了3DGS应用的最新进展：首先回顾3DGS的重建基础，继而阐述问题形式化、2D基础模型及为下游3DGS应用提供参考的NeRF相关研究领域。我们将3DGS应用归纳为三大基础任务——分割、编辑与生成，并涵盖基于或紧密耦合这些核心能力的附加功能型应用。针对每类任务，我们总结了代表性方法、监督策略与学习范式，重点揭示共享设计原则与新兴趋势。同时归纳常用数据集与评估协议，并基于公开基准对近期方法展开对比分析。为支持持续研究，我们维护着持续更新的论文、代码与资源仓库，详见https://github.com/heshuting555/Awesome-3DGS-Applications。