SUCCESS-GS：紧凑性与压缩的调查：为高效静态和动态高斯点划法服务
SUCCESS-GS: Survey of Compactness and Compression for Efficient Static and Dynamic Gaussian Splatting
AI总结：
本文综述了高效3D和4D高斯点划法技术，探讨了参数压缩与重构压缩两大方向，并讨论了其应用与未来研究方向。
摘要：
3D高斯点划法（3DGS）已发展为一种强大的显式表示方法，能够实现实时、高保真的3D重建和新视角合成。然而，其实际应用受到存储和渲染数百万个高斯点所需的大量内存和计算需求的限制。在4D动态场景中，这些挑战变得更加严重。为了解决这些问题，高效高斯点划法领域迅速发展，提出了减少冗余同时保持重建质量的方法。本文提供了首个统一的3D和4D高斯点划法技术概述。对于3D和4D场景，我们系统地将现有方法分为两大方向：参数压缩和重构压缩，并全面总结了每个类别中的核心思想和方法论趋势。我们进一步涵盖了广泛使用的数据集、评估指标和代表性基准比较。最后，我们讨论了当前的局限性，并概述了朝着可扩展、紧凑和实时的高斯点划法方向的研究前景，以实现静态和动态3D场景表示。
英文摘要：
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.
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。
