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.
在这项研究中，我们探讨了如何高效地用有限数量的高斯函数表示场景的挑战。我们的分析从传统图形学和二维计算机视觉转向点云的视角，强调高斯表示的低效空间分布是模型性能的一个关键限制。为了解决这个问题，我们引入了密集化策略，包括模糊分裂和深度重新初始化，以及通过高斯二值化和采样来简化。这些技术重新组织了高斯的空间位置，导致在渲染质量、资源消耗和存储压缩方面在各种数据集和基准测试中的显著改进。我们提出的Mini-Splatting方法与原始光栅化管线无缝集成，为未来基于高斯喷溅的研究提供了一个强大的基线。