3DGS.zip：3D高斯散射压缩方法综述
3DGS.zip: A survey on 3D Gaussian Splatting Compression Methods
AI总结：
本文综述了3DGS压缩方法，探讨了压缩与紧缩技术，旨在提高3DGS的效率和实用性，通过减少文件大小和高斯数量来优化质量和性能。
摘要：
3D高斯散射（3DGS）作为一种实时辐射场渲染技术，因其质量和速度的先进性能而崭露头角。3DGS将场景建模为三维高斯集合，并通过优化额外属性以符合场景的几何和视觉特性。尽管其在渲染速度和图像保真度方面具有优势，但其显著的存储和内存需求限制了其在移动设备或头显中的应用。为解决这些挑战并推动3DGS的实用性，本文提供了对压缩和紧缩技术的全面详细分析。我们将现有方法分为压缩（减少文件大小）和紧缩（减少高斯数量）两类。两种方法均旨在维持或提升质量，分别通过最小化其各自属性：压缩通过最小化文件大小，紧缩通过最小化高斯数量。我们介绍了所分析方法的基本数学概念，以及关键的实现细节和设计选择。本文详尽讨论了方法之间的相似性和差异性，以及各自的优势和劣势。我们建立了基于关键性能指标和数据集的统一框架，以比较这些方法。由于这些方法在短时间内并行发展，目前尚无全面的比较。本文首次提出一个统一的框架来评估3DGS压缩技术。我们维护一个网站，定期更新新兴方法：https://w-m.github.io/3dgs-compression-survey/。
英文摘要：
3D Gaussian Splatting (3DGS) has emerged as a cutting-edge technique for real-time radiance field rendering, offering state-of-the-art performance in terms of both quality and speed. 3DGS models a scene as a collection of three-dimensional Gaussians, with additional attributes optimized to conform to the scene's geometric and visual properties. Despite its advantages in rendering speed and image fidelity, 3DGS is limited by its significant storage and memory demands. These high demands make 3DGS impractical for mobile devices or headsets, reducing its applicability in important areas of computer graphics. To address these challenges and advance the practicality of 3DGS, this survey provides a comprehensive and detailed examination of compression and compaction techniques developed to make 3DGS more efficient. We classify existing methods into two categories: compression, which focuses on reducing file size, and compaction, which aims to minimize the number of Gaussians. Both methods aim to maintain or improve quality, each by minimizing its respective attribute: file size for compression and Gaussian count for compaction. We introduce the basic mathematical concepts underlying the analyzed methods, as well as key implementation details and design choices. Our report thoroughly discusses similarities and differences among the methods, as well as their respective advantages and disadvantages. We establish a consistent framework for comparing the surveyed methods based on key performance metrics and datasets. Specifically, since these methods have been developed in parallel and over a short period of time, currently, no comprehensive comparison exists. This survey, for the first time, presents a unified framework to evaluate 3DGS compression techniques. We maintain a website that will be regularly updated with emerging methods: https://w-m.github.io/3dgs-compression-survey/ .
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
