Mini-Splatting：用受限数量的高斯表示场景
Mini-Splatting: Representing Scenes with a Constrained Number of Gaussians
AI总结：
本研究提出Mini-Splatting方法，通过模糊分裂、深度重新初始化等密集化与简化策略优化高斯空间分布，在受限数量下提升渲染质量并降低资源消耗，为Gaussian-Splatting研究提供强基线。
摘要：
在本研究中，我们探讨了用受限数量的高斯高效表示场景的挑战。我们的分析从传统图形学和2D计算机视觉转向点云视角，指出高斯表示的低效空间分布是模型性能的关键限制。为此，我们引入了包括模糊分裂和深度重新初始化的密集化策略，以及通过保持相交和采样的简化策略。这些技术重新组织了高斯的空间位置，在渲染质量、资源消耗和存储压缩方面，于多个数据集和基准上取得了显著改进。我们的Mini-Splatting与原始光栅化管道无缝集成，为未来基于Gaussian-Splatting的研究提供了强基线。代码可用。
英文摘要：
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 intersection preserving 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 Mini-Splatting integrates seamlessly with the original rasterization pipeline, providing a strong baseline for future research in Gaussian-Splatting-based works. \href{https://github.com/fatPeter/mini-splatting}{Code is available}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
