优化高斯点空间分布，用更少点实现更好3D渲染效果
GS^2: Graph-based Spatial Distribution Optimization for Compact 3D Gaussian Splatting
- 基于ELBO的自适应加密策略控制点数增长
- 仅用12.5%点数即达更高PSNR，显著提升压缩率
- 适合追求高效3D重建与实时渲染的开发者
3D高斯点阵（3DGS）在新视角合成和实时渲染中表现卓越，但其高内存开销源于海量高斯点。现有剪枝类方法虽降低内存占用，却常损害空间一致性并引发渲染伪影。为此，本文提出图结构空间分布优化方法（GS²），通过证据下界（ELBO）驱动的自适应加密策略自动控制点数增长；设计透明度感知的渐进式剪枝策略，动态移除低透明度点以进一步减少内存；并引入图结构特征编码模块，利用特征引导点位移动来优化空间分布。大量实验表明，GS²在仅使用约12.5%高斯点的情况下，仍能实现优于原始3DGS的重建质量（更高PSNR），且全面超越所有对比基线，在渲染质量和内存效率上均表现优异。
原文摘要 · Abstract (English)
3D Gaussian Splatting (3DGS) has demonstrated breakthrough performance in novel view synthesis and real-time rendering. Nevertheless, its practicality is constrained by the high memory cost due to a huge number of Gaussian points. Many pruning-based 3DGS variants have been proposed for memory saving, but often compromise spatial consistency and may lead to rendering artifacts. To address this issue, we propose graph-based spatial distribution optimization for compact 3D Gaussian Splatting (GS\textasciicircum2), which enhances reconstruction quality by optimizing the spatial distribution of Gaussian points. Specifically, we introduce an evidence lower bound (ELBO)-based adaptive densification strategy that automatically controls the densification process. In addition, an opacity-aware progressive pruning strategy is proposed to further reduce memory consumption by dynamically removing low-opacity Gaussian points. Furthermore, we propose a graph-based feature encoding module to adjust the spatial distribution via feature-guided point shifting. Extensive experiments validate that GS\textasciicircum2 achieves a compact Gaussian representation while delivering superior rendering quality. Compared with 3DGS, it achieves higher PSNR with only about 12.5\% Gaussian points. Furthermore, it outperforms all compared baselines in both rendering quality and memory efficiency.
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