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 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 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，三维高斯溅射（3DGS）作为神经辐射场（NeRF）的高效竞争性技术方案，近期实现了实时高保真度照片级真实感渲染。除新视角合成外，3DGS显式且紧凑的特性使其在需要几何与语义理解的下游应用中展现出广泛潜力。本综述系统梳理了3DGS应用的最新进展：首先阐释支撑3DGS应用语义理解与控制能力的二维基础模型，继而回顾为3DGS方法提供理论参照的NeRF相关研究。我们将3DGS应用归纳为三大基础任务——分割、编辑与生成，并梳理了基于这些核心能力构建的衍生功能应用。针对每类任务，本文总结了代表性方法、监督策略与学习范式，着重分析其共性设计原则与前沿发展趋势。同时汇总了常用数据集与评估协议，并通过公开基准测试对现有方法进行对比分析。为支持持续研究，我们在https://github.com/heshuting555/Awesome-3DGS-Applications 维护包含论文、代码及相关资源的动态更新库。