In this study, we explore the essential challenge of fast scene optimization for Gaussian Splatting. Through a thorough analysis of the geometry modeling process, we reveal that dense point clouds can be effectively reconstructed early in optimization through Gaussian representations. This insight leads to our approach of aggressive Gaussian densification, which provides a more efficient alternative to conventional progressive densification methods. By significantly increasing the number of critical Gaussians, we enhance the model capacity to capture dense scene geometry at the early stage of optimization. This strategy is seamlessly integrated into the Mini-Splatting densification and simplification framework, enabling rapid convergence without compromising quality. Additionally, we introduce visibility culling within Gaussian Splatting, leveraging per-view Gaussian importance as precomputed visibility to accelerate the optimization process. Our Mini-Splatting2 achieves a balanced trade-off among optimization time, the number of Gaussians, and rendering quality, establishing a strong baseline for future Gaussian-Splatting-based works. Our work sets the stage for more efficient, high-quality 3D scene modeling in real-world applications.
在本研究中，我们探讨了 Gaussian Splatting 快速场景优化的核心挑战。通过对几何建模过程的深入分析，我们发现可以通过高斯表示在优化的早期阶段有效地重建稠密点云。基于这一洞察，我们提出了 激进的高斯密化策略，作为传统渐进密化方法的一种更高效替代方案。通过显著增加关键高斯的数量，我们增强了模型在优化初期捕获稠密场景几何的能力。 该策略无缝集成到 Mini-Splatting 的密化与简化框架中，实现了快速收敛且不牺牲质量。此外，我们在高斯分布中引入了 可见性剔除，利用每视角的高斯重要性作为预计算的可见性指标，加速优化过程。 我们的 Mini-Splatting2 在优化时间、高斯数量和渲染质量之间达成了良好的平衡，为未来基于高斯分布的研究奠定了强大的基线。我们的工作为现实应用中的高效、高质量 3D 场景建模铺平了道路。