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, and the code will be made available no matter acceptance.

本研究解决了高斯点溅射快速场景优化的关键挑战。我们提出了一种激进的高斯稠密化方法，显著提高了在优化初期捕捉密集场景几何形状的能力，进而实现优化时间、点数与渲染质量之间的良好平衡。这一方法为未来基于高斯点溅射的工作奠定了坚实基础。

迷你点溅射2：通过激进的高斯稠密化在几分钟内构建360场景