SUCCESS-GS：面向高效静态与动态高斯泼溅的紧凑性与压缩研究

摘要

3D高斯泼溅（3DGS）作为一种强大的显式表示方法，已能实现实时高保真度的三维重建与新视角合成。然而，该方法在存储和渲染数百万高斯粒子时所需的巨大内存与计算量，制约了其实际应用。这些挑战在四维动态场景中尤为严峻。为解决这些问题，高效高斯泼溅技术领域迅速发展，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高斯泼溅技术进行了统一综述：针对静态与动态场景，我们系统地将现有方法归类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发展趋势；进一步梳理了广泛使用的数据集、评估指标及代表性基准对比；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、实时高斯泼溅技术的潜在研究方向。

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泼溅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

Support