初始点云密度影响3D高斯溅射的视觉与几何质量，密集初始化更利于泛化和结构一致性。
The Role of Initialization in 3D Gaussian Splatting
- 用不同密度的点云作为初始结构，研究其对3DGS性能的影响。
- 密集初始化虽不提升整体视觉效果，但显著改善离轨迹视角的泛化能力。
- 适合关注场景几何一致性与新视角生成的科研与工程应用。
3D高斯溅射（3DGS）因其高效性和出色的视觉质量，已成为真实感新视角合成（NVS）的首选方法。3DGS通过一组3D高斯表示场景，参数包括位置、空间范围和视角相关的颜色。从初始点云出发，3DGS通过优化高斯参数以尽可能准确重建训练图像。通常使用稀疏的运动恢复结构（SfM）点云作为初始化。为获得完整场景表示，3DGS方法依赖于密集化阶段。本文系统研究了初始化对3DGS NVS性能与几何质量的影响，采用多种密集化策略。结果表明，尽管密集初始化在强密集化下未带来一致的视觉提升，但有助于提升对离轨迹视角的泛化能力，并显著改善场景表示的几何一致性。
原文摘要 · Abstract (English)
3D Gaussian Splatting (3DGS) has become the method of choice for photo-realistic novel view synthesis (NVS), due to its efficiency and compelling visual quality. 3DGS represents the scene through a set of 3D Gaussians, parameterized by their position, spatial extent, and view-dependent color. Starting from an initial point cloud, 3DGS refines the Gaussians' parameters as to reconstruct a set of training images as accurately as possible. Typically, a sparse Structure-from-Motion point cloud is used as initialization. In order to obtain a full scene representation, 3DGS methods thus rely on a densification stage. In this paper, we systematically study how initialization affects 3DGS NVS performance and geometric quality, using several densification strategies. We show that dense initialization does not lead to consistent visual improvements when paired with strong densification. Despite that, it can help in generalization to off-trajectory views and significantly improves geometric consistency of the scene representation.
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