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.
3D高斯平滑（3DGS）的出现大大加快了新视角合成的渲染速度。与通过位置和视点条件神经网络表示3D场景的神经隐式表示，如Neural Radiance Fields（NeRF）不同，3D高斯平滑利用一组高斯椭球来模拟场景，从而可以通过将高斯椭球光栅化成图像来实现高效渲染。除了快速渲染速度之外，3D高斯平滑的显式表示还便于执行编辑任务，如动态重建、几何编辑和物理模拟。考虑到这一领域的迅速变化和不断增长的工作数量，我们提出了一篇关于最近3D高斯平滑方法的文献综述，这些方法大致可以按功能分为3D重建、3D编辑和其他下游应用。还阐述了传统的基于点的渲染方法和3D高斯平滑的渲染公式，以更好地理解这项技术。这项调查旨在帮助初学者快速进入这一领域，并为有经验的研究人员提供一个全面的概述，这可以刺激3D高斯平滑表示的未来发展。