3D Gaussian Splat (3DGS) enables high-fidelity, real-time novel view synthesis by representing scenes with large sets of anisotropic primitives, but often requires millions of Splats, incurring significant storage and transmission costs. Most existing compression methods rely on GPU-intensive post-training optimization with calibrated images, limiting practical deployment. We introduce NanoGS, a training-free and lightweight framework for Gaussian Splat simplification. Instead of relying on image-based rendering supervision, NanoGS formulates simplification as local pairwise merging over a sparse spatial graph. The method approximates a pair of Gaussians with a single primitive using mass preserved moment matching and evaluates merge quality through a principled merge cost between the original mixture and its approximation. By restricting merge candidates to local neighborhoods and selecting compatible pairs efficiently, NanoGS produces compact Gaussian representations while preserving scene structure and appearance. NanoGS operates directly on existing Gaussian Splat models, runs efficiently on CPU, and preserves the standard 3DGS parameterization, enabling seamless integration with existing rendering pipelines. Experiments demonstrate that NanoGS substantially reduces primitive count while maintaining high rendering fidelity, providing an efficient and practical solution for Gaussian Splat simplification. Our project website is available at https://saliteta.github.io/NanoGS/.

翻译：三维高斯泼溅（3DGS）通过大量各向异性基元表示场景，实现了高保真度的实时新视角合成，但通常需要数百万个泼溅单元，导致显著的存储与传输开销。现有压缩方法大多依赖基于校准图像的GPU密集型训练后优化，限制了实际部署。本文提出NanoGS——一种免训练的轻量级高斯泼溅简化框架。该方法摒弃基于图像的渲染监督，将简化问题建模为稀疏空间图上的局部成对合并操作。通过质量守恒的矩匹配技术将成对高斯分布近似为单一基元，并基于原始混合分布与其近似之间的理论合并代价评估合并质量。通过将合并候选限制在局部邻域并高效筛选兼容对，NanoGS能在保持场景结构与外观的前提下生成紧凑的高斯表示。该框架可直接处理现有高斯泼溅模型，在CPU上高效运行，且保持标准3DGS参数化形式，实现与现有渲染管线的无缝集成。实验表明，NanoGS在维持高渲染保真度的同时显著降低基元数量，为高斯泼溅简化提供了高效实用的解决方案。项目网站详见https://saliteta.github.io/NanoGS/。