MesonGS++: Post-training Compression of 3D Gaussian Splatting with Hyperparameter Searching

• URL: http://arxiv.org/abs/2604.26799v2
• Date: Thu, 07 May 2026 06:27:58 GMT
• Title: MesonGS++: Post-training Compression of 3D Gaussian Splatting with Hyperparameter Searching
• Abstract summary: 3D Gaussian Splatting (3DGS) achieves high-quality novel view with real-time rendering, but its storage cost remains prohibitive for practical deployment.<n>We propose MesonGS, a size-aware post-training for 3D Gaussian compression. On the side, MesonGS combines joint importance-based pruning, octree geometry, transformations, selective vector quantization for higher-degree spherical harmonics, and group-wise mixed-precision quantization with entropy coding.
• Score: 27.114461961869782
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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

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