3D Gaussian Splatting (3DGS) is a promising neural scene representation for real-time rendering, but trained models often suffer from large memory footprints, limiting deployment on less powerful devices. Existing compression techniques often lead to architectures with several additional trainable parameters. While achieving outstanding compression ratios, they introduce noticeable drops in image quality. In this work, we introduce the first dictionary-learning-based compression framework for 3DGS. The proposed post-training compression pipeline can be deployed in virtually any 3DGS model without the need for re-training or modifications to existing 3DGS models. Our compression framework is straightforward to implement, yet provides significant compression capabilities, preserves image quality, and improves real-time rendering performance. Across 13 benchmark scenes, our approach achieves an average compression ratio of 3.95x, 3.10x, and 4.55x when applied to 3DGS, 3DGS-MCMC, and PixelGS, respectively. This yields consistent rendering speedups of 23.3%, 24.3%, and 25.3%, while maintaining image quality.

翻译：三维高斯泼溅（3DGS）是一种具有前景的实时渲染神经场景表示方法，但训练后的模型往往存在内存占用过大的问题，限制了其在低性能设备上的部署。现有压缩技术常引入包含多个额外可训练参数的网络架构，虽能实现出色的压缩比，但会导致图像质量明显下降。本文提出首个基于字典学习的3DGS压缩框架。该后训练压缩流程可应用于几乎所有3DGS模型，无需重新训练或修改现有模型架构。本压缩框架实现简便，兼具显著的压缩能力、图像质量保持能力及实时渲染性能提升。在13个基准场景中，本方法对3DGS、3DGS-MCMC和PixelGS模型分别实现平均3.95倍、3.10倍和4.55倍的压缩比，同时带来23.3%、24.3%和25.3%的持续渲染加速，且保持图像质量不变。