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$\times$ efficiency increase with quality comparable to state-of-the-art methods.

翻译：有效的压缩技术对于三维高斯（3DGS）适应不同存储与传输条件至关重要。然而，现有方法难以在保持最优质量的同时满足尺寸约束。本文提出SizeGS，一个能在指定尺寸预算内压缩3DGS并优化视觉质量的框架。我们首先构建尺寸估计器，以建立文件大小与超参数之间的明确关系。利用该估计器，我们将混合精度量化（MPQ）应用于3DGS属性，并构建双层MPQ结构——属性间与属性内层级——以在尺寸约束下优化视觉质量。在属性间层级，我们将组合优化问题建模为0-1整数线性规划，高效求解并为各属性通道分配比特宽度。在属性内层级，我们将每个属性通道划分为向量块，基于属性间层级得到的最优比特宽度对每个向量进行量化。块长度由动态规划确定。结合尺寸估计器与MPQ，我们开发了校准算法，可在10分钟内确定最优超参数，在视觉质量与先进方法相当的前提下，实现1.69倍的效率提升。