The emergence of 3D Gaussian Splatting (3DGS) has greatly accelerated the rendering speed of novel view synthesis. Unlike neural implicit representations like Neural Radiance Fields (NeRF) that represent a 3D scene with position and viewpoint-conditioned neural networks, 3D Gaussian Splatting utilizes a set of Gaussian ellipsoids to model the scene so that efficient rendering can be accomplished by rasterizing Gaussian ellipsoids into images. Apart from the fast rendering speed, the explicit representation of 3D Gaussian Splatting facilitates editing tasks like dynamic reconstruction, geometry editing, and physical simulation. Considering the rapid change and growing number of works in this field, we present a literature review of recent 3D Gaussian Splatting methods, which can be roughly classified into 3D reconstruction, 3D editing, and other downstream applications by functionality. Traditional point-based rendering methods and the rendering formulation of 3D Gaussian Splatting are also illustrated for a better understanding of this technique. This survey aims to help beginners get into this field quickly and provide experienced researchers with a comprehensive overview, which can stimulate the future development of the 3D Gaussian Splatting representation.

翻译：三维高斯泼溅（3D Gaussian Splatting, 3DGS）的诞生极大加速了新视角合成的渲染速度。与神经辐射场（NeRF）等使用位置和视角条件神经网络表示三维场景的神经隐式表征不同，3DGS通过一组高斯椭球体对场景进行建模，通过将高斯椭球体光栅化为图像实现高效渲染。除了渲染速度优势外，3DGS的显式表征特性还便于动态重建、几何编辑和物理模拟等编辑任务。鉴于该领域研究进展迅速且论文数量激增，本文对近期3DGS方法进行了文献综述，按功能将其大致分为三维重建、三维编辑及其他下游应用三类。为帮助读者更好理解该技术，本文还阐述了传统基于点云的渲染方法和3DGS的渲染框架。本综述旨在帮助初学者快速入门该领域，并为资深研究者提供全面概览，以期推动3DGS表征技术的未来发展。