3D Gaussian Splatting (3DGS) achieves high-quality novel view synthesis with real-time rendering, but its storage cost remains prohibitive for practical deployment. Existing post-training compression methods still rely on many coupled hyperparameters across pruning, transformation, quantization, and entropy coding, making it difficult to control the final compressed size and fully exploit the rate-distortion trade-off. We propose MesonGS++, a size-aware post-training codec for 3D Gaussian compression. On the codec side, MesonGS++ combines joint importance-based pruning, octree geometry coding, attribute transformation, selective vector quantization for higher-degree spherical harmonics, and group-wise mixed-precision quantization with entropy coding. On the configuration side, it treats the reserve ratio and bit-width allocation as the dominant rate-distortion knobs and jointly optimizes them under a target storage budget via discrete sampling and 0--1 integer linear programming. We further propose a linear size estimator and a CUDA parallel quantization operator to accelerate the hyperparameter searching process. Extensive experiments show that MesonGS++ achieves over 34$\times$ compression while preserving rendering fidelity, outperforming state-of-the-art post-training methods and accurately meeting target size budgets. Remarkably, without any training, MesonGS++ can even surpass the PSNR of vanilla 3DGS at a 20$\times$ compression rate on the Stump scene. Our code is available at https://github.com/mmlab-sigs/mesongs_plus

翻译：3D高斯溅射（3DGS）以实时渲染实现了高质量的新视角合成，但其存储成本仍对实际部署构成阻碍。现有的后训练压缩方法仍依赖众多耦合的超参数，这些超参数涉及剪枝、变换、量化和熵编码，使得最终压缩尺寸难以控制，且无法充分利用率失真权衡。我们提出MesonGS++，一种面向3D高斯压缩的尺寸感知后训练编解码器。在编解码器层面，MesonGS++结合了基于联合重要性的剪枝、八叉树几何编码、属性变换、球面谐波高阶分量选择性向量量化，以及基于熵编码的分组混合精度量化。在配置层面，该方法将保留率和比特宽度分配视为主要的率失真调节旋钮，并通过离散采样和0-1整数线性规划在目标存储预算下联合优化二者。我们还提出线性尺寸估计器和CUDA并行量化算子，以加速超参数搜索过程。大量实验表明，MesonGS++在保持渲染保真度的同时实现了超过34倍的压缩，优于现有最先进的后训练方法，并能精确满足目标尺寸预算。值得注意的是，无需任何训练，MesonGS++在Stump场景中甚至能以20倍压缩率超越原始3DGS的PSNR。我们的代码已开源在https://github.com/mmlab-sigs/mesongs_plus。