SUCCESS-GS：面向高效静态与动态高斯溅射的紧凑性与压缩研究

摘要

3D高斯溅射（3DGS）作为一种强大的显式表示方法，已能够实现实时高保真三维重建与新视角合成。然而，其实际应用受限于存储和渲染数百万高斯粒子所需的巨大内存与计算量，这一挑战在四维动态场景中尤为突出。为解决这些问题，高效高斯溅射技术领域迅速发展，提出了多种在保持重建质量的同时减少冗余的方法。本文首次对高效3D与4D高斯溅射技术进行了系统性梳理：针对静态与动态场景，将现有方法划分为参数量压缩与结构重组压缩两大方向，全面总结了各类方法的核心思想与技术趋势；进一步涵盖了广泛使用的数据集、评估指标及代表性基准测试对比；最后讨论了当前技术局限，并展望了面向静态与动态三维场景的可扩展、紧凑、实时高斯溅射技术的未来研究方向。

English

3D Gaussian Splatting (3DGS) has emerged as a powerful explicit representation enabling real-time, high-fidelity 3D reconstruction and novel view synthesis. However, its practical use is hindered by the massive memory and computational demands required to store and render millions of Gaussians. These challenges become even more severe in 4D dynamic scenes. To address these issues, the field of Efficient Gaussian Splatting has rapidly evolved, proposing methods that reduce redundancy while preserving reconstruction quality. This survey provides the first unified overview of efficient 3D and 4D Gaussian Splatting techniques. For both 3D and 4D settings, we systematically categorize existing methods into two major directions, Parameter Compression and Restructuring Compression, and comprehensively summarize the core ideas and methodological trends within each category. We further cover widely used datasets, evaluation metrics, and representative benchmark comparisons. Finally, we discuss current limitations and outline promising research directions toward scalable, compact, and real-time Gaussian Splatting for both static and dynamic 3D scene representation.

SUCCESS-GS：面向高效静态与动态高斯溅射的紧凑性与压缩研究

SUCCESS-GS: Survey of Compactness and Compression for Efficient Static and Dynamic Gaussian Splatting

摘要

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