CVT-GS: Learning to Simplify 3D Gaussian Splatting with Centroidal Voronoi Tessellation

2026-09-08

AI Summary

本文提出CVT-GS方法，通过几何感知的Voronoi图和神经单元合并技术简化3D高斯点云，在不牺牲视觉保真度的前提下大幅减少存储和计算开销。

Abstract

While 3D Gaussian Splatting (3DGS) has emerged as a powerful representation for real-time novel view synthesis, rendering high-fidelity scenes often relies on a massive number of Gaussian primitives, incurring substantial storage and computational overhead. Existing simplification techniques are largely intrusive, requiring training-time pruning, architectural modifications, or computationally expensive per-scene fine-tuning. These drawbacks limit their deployment on off-the-shelf pretrained models. In this paper, we propose CVT-GS, a novel optimization-free post-hoc simplification framework that directly compresses trained 3DGS scenes without sacrificing visual fidelity. Our approach first constructs spatially coherent cells over Gaussian centers via a geometry-aware Centroidal Voronoi Tessellation (CVT). Subsequently, a lightweight neural cell merger predicts the geometry and appearance of a single, highly representative Gaussian primitive for each cell under differentiable rendering supervision. By formulating simplification as a rendering-aware many-to-one merging process rather than naive primitive pruning, CVT-GS outputs a standard 3DGS scene that is seamlessly compatible with existing renderers. Experiments on various datasets demonstrate the superiority of our method. Notably, when achieving a 100-fold reduction in Gaussian points, our method operates 12 times faster than state-of-the-art methods while improving the PSNR by 1.3 dB.

Problem

• 3D Gaussian Splatting
• storage and computational overhead
• simplification techniques
• pretrained models

Innovation

• Centroidal Voronoi Tessellation
• post-hoc simplification
• geometry-aware
• neural cell merger
• rendering-aware merging