Effective compression technology is crucial for 3DGS to adapt to varying storage and transmission conditions. However, existing methods fail to address size constraints while maintaining optimal quality. In this paper, we introduce SizeGS, a framework that compresses 3DGS within a specified size budget while optimizing visual quality. We start with a size estimator to establish a clear relationship between file size and hyperparameters. Leveraging this estimator, we incorporate mixed precision quantization (MPQ) into 3DGS attributes, structuring MPQ in two hierarchical level -- inter-attribute and intra-attribute -- to optimize visual quality under the size constraint. At the inter-attribute level, we assign bit-widths to each attribute channel by formulating the combinatorial optimization as a 0-1 integer linear program, which can be efficiently solved. At the intra-attribute level, we divide each attribute channel into blocks of vectors, quantizing each vector based on the optimal bit-width derived at the inter-attribute level. Dynamic programming determines block lengths. Using the size estimator and MPQ, we develop a calibrated algorithm to identify optimal hyperparameters in just 10 minutes, achieving a 1.69× efficiency increase with quality comparable to state-of-the-art methods.
高效的压缩技术对于3D高斯点云（3D Gaussian Splatting, 3DGS）适应多变的存储和传输条件至关重要。然而，现有方法在满足存储大小限制的同时保持最佳质量方面表现不足。为此，本文提出 SizeGS，一种框架化方法，能够在指定的大小预算内压缩3DGS，同时优化视觉质量。 我们首先设计了一个 大小估计器，用于建立文件大小与超参数之间的明确关系。在此基础上，我们将混合精度量化（Mixed Precision Quantization, MPQ）引入3DGS属性，并在两个层级中结构化MPQ：跨属性层级（inter-attribute）和属性内层级（intra-attribute），以在大小限制下优化视觉质量。跨属性层级：通过将组合优化问题表述为0-1整数线性规划，为每个属性通道分配位宽，该问题能够高效求解。属性内层级：将每个属性通道划分为向量块，并基于跨属性层级确定的最佳位宽对每个向量进行量化。块长度则通过动态规划方法确定。 利用大小估计器和MPQ，我们开发了一种校准算法，仅需10分钟即可识别最佳超参数。在实验中，SizeGS实现了1.69倍的效率提升，并且在视觉质量上可媲美最新的先进方法。这一方法为3DGS在存储受限环境中的高效应用提供了新工具，同时保持了优异的视觉效果。