In this study, we explore the challenge of efficiently representing scenes
with a constrained number of Gaussians. Our analysis shifts from traditional
graphics and 2D computer vision to the perspective of point clouds,
highlighting the inefficient spatial distribution of Gaussian representation as
a key limitation in model performance. To address this, we introduce strategies
for densification including blur split and depth reinitialization, and
simplification through Gaussian binarization and sampling. These techniques
reorganize the spatial positions of the Gaussians, resulting in significant
improvements across various datasets and benchmarks in terms of rendering
quality, resource consumption, and storage compression. Our proposed
Mini-Splatting method integrates seamlessly with the original rasterization
pipeline, providing a strong baseline for future research in
Gaussian-Splatting-based works.

通过高斯分布，本研究探讨了在受限高斯数目条件下高效表示场景的挑战；通过模糊分割、深度重新初始化、高斯二值化和采样等技术，我们重新组织了高斯分布的空间位置，从而显著提高了渲染质量、资源消耗和存储压缩等方面的性能，并提出了一种与原始光栅化流程无缝集成的 Mini-Splatting 方法，为基于高斯喷洒的研究提供了一个强有力的基准。