NanoGS: Training-Free and Lightweight Gaussian Splat Simplification

3D Gaussian Splat (3DGS) enables high-fidelity, real-timenovel view synthesis by representing scenes with large sets of anisotropicprimitives, but often requires millions of Splats, incurring significant stor-age and transmission costs. Most existing compression methods rely onGPU-intensive post-training optimization with calibrated images, limit-ing practical deployment. We introduce NanoGS, a training-free andlightweight framework for Gaussian Splat simplification. Instead of re-lying on image-based rendering supervision, NanoGS formulates sim-plification as local pairwise merging over a sparse spatial graph. Themethod approximates a pair of Gaussians with a single primitive usingmass preserved moment matching and evaluates merge quality througha principled merge cost between the original mixture and its approx-imation. By restricting merge candidates to local neighborhoods andselecting compatible pairs efficiently, NanoGS produces compact Gaus-sian representations while preserving scene structure and appearance.NanoGS operates directly on existing Gaussian Splat models, runs ef-ficiently on CPU, and preserves the standard 3DGS parameterization,enabling seamless integration with existing rendering pipelines. Experi-ments demonstrate that NanoGS substantially reduces primitive countwhile maintaining high rendering fidelity, providing an efficient and prac-tical solution for Gaussian Splat simplification. Our project website isavailable at https://saliteta.github.io/NanoGS/ .