GS^2: Graph-based Spatial Distribution Optimization for Compact 3D Gaussian Splatting

Abstract

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.

Figures (7)

• Figure 1: Comparison of rendering quality and spatial distribution. We report the PSNR and number of Gaussian points ($N_{\textrm{GS}}$, in millions) for each method. While a SOTA solution (LightGaussian fan2024lightgaussian) compromises spatial consistency with visual artifacts, our method produces a more uniform and coherent distribution.
• Figure 2: Comparison of rendering quality (PSNR) and number of Gaussian points ($N_{\textrm{GS}}$, in millions ) for various methods on the Mip-NeRF360 dataset.
• Figure 3: The pipeline of our method. We propose an Adaptive Densification and Pruning (ADP) module that adaptively increases point density in phase 1 and prunes low-opacity points in phase 2. To improve the spatial distribution after pruning, we introduce a Graph-based Spatial Distribution Optimization (GSDO) module, which refines spatial distribution in phase 3 using a lightweight graph-based feature encoder with global alignment and local smoothness losses.
• Figure 4: ELBO-guided adaptive densification.
• Figure 5: Analyzing the impact of optimization. Low-opacity Gaussian points ($\text{opacity} \leq 0.1$) constitute a considerable portion of the total. For instance, in the Lighthouse scene from the Tanks & Temples dataset, they account for approximately 40% of all Gaussian points.