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

TL;DR

This survey reviews recent advances in efficient Gaussian Splatting techniques, focusing on reducing memory and computational costs for static and dynamic 3D scene representations while maintaining high quality.

Contribution

It provides the first comprehensive overview and categorization of 3D and 4D Gaussian Splatting methods, highlighting core ideas and research trends.

Findings

Categorizes methods into Parameter and Restructuring Compression

Summarizes datasets, metrics, and benchmarks used in the field

Discusses limitations and future research directions

Abstract

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.