3D Gaussian splatting technologies and extensions: A review

IF 6.5 2区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Neurocomputing Pub Date : 2025-10-01 DOI:10.1016/j.neucom.2025.131629

Fengkai Luan , Siliang Sun , Hu Zhang, Yong Yin, Ke Wang, Jiaxing Yang

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引用次数: 0

Abstract

In recent years, 3D Gaussian Splatting (3DGS) has achieved remarkable progress in the field of novel view synthesis. Unlike implicit neural radiance field (NeRF) methods that primarily focus on positional and viewpoint transformations, 3DGS leverages millions of Gaussian ellipsoids for scene reconstruction and employs parallel differentiable rasterization to substantially improve rendering efficiency. Given the rapid advancement and promising prospects of this technique, this survey presents a systematic overview of recent developments in 3DGS. We provide a detailed exposition of the fundamental theory underlying 3DGS, along with relevant benchmark datasets. Uniquely, this work organizes existing optimization strategies according to the stages of the Gaussian splatting pipeline. In addition, we review various downstream applications based on 3DGS and discuss prospective research directions. This survey aims to serve as a valuable reference for researchers across all stages of engagement and to foster further advancements in 3DGS.

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三维高斯溅射技术及其扩展：综述

近年来，三维高斯溅射（3DGS）技术在新视图合成领域取得了显著进展。与隐式神经辐射场（NeRF）方法主要关注位置和视点转换不同，3DGS利用数百万高斯椭球进行场景重建，并采用并行可微光栅化来大幅提高渲染效率。鉴于该技术的快速发展和广阔的前景，本调查对3DGS的最新发展进行了系统的概述。我们提供了3DGS基础理论的详细阐述，以及相关的基准数据集。独特的是，这项工作根据高斯溅射管道的阶段组织现有的优化策略。此外，我们回顾了基于3DGS的各种下游应用，并讨论了未来的研究方向。这项调查旨在为研究人员提供有价值的参考，并促进3DGS的进一步发展。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Neurocomputing 工程技术-计算机：人工智能

CiteScore

13.10

自引率

10.00%

发文量

1382

审稿时长

70 days

期刊介绍： Neurocomputing publishes articles describing recent fundamental contributions in the field of neurocomputing. Neurocomputing theory, practice and applications are the essential topics being covered.