首份系统综述高效3D/4D高斯溅射压缩方法
SUCCESS-GS: Survey of Compactness and Compression for Efficient Static and Dynamic Gaussian Splatting
- 按参数压缩与结构压缩两大方向分类梳理技术
- 涵盖主流数据集、评估指标与基准对比结果
- 适合关注3D重建与动态场景实时渲染的研究者
3D高斯溅射（3DGS）作为强大的显式表示方法，可实现实时、高保真3D重建与新视角合成。但其实际应用受限于存储和渲染数百万个高斯分布所需的巨大内存与计算开销，动态4D场景下问题更为严峻。为此，高效高斯溅射领域迅速发展，提出多种减少冗余同时保持重建质量的方法。本文首次提供3D与4D高效高斯溅射技术的统一综述，系统将现有方法分为参数压缩与重构压缩两大类，全面总结各类核心思想与方法趋势，并涵盖常用数据集、评估指标及代表性基准比较。最后，讨论当前局限并展望可扩展、紧凑且实时的静态与动态3D场景表示的未来研究方向。
原文摘要 · Abstract (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.
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