首次让高斯点阵用多种几何形状重建表面，效果更准。
A Mixed-Primitive-based Gaussian Splatting Method for Surface Reconstruction
- 用多种几何基元（如椭圆、椭球）混合表示物体表面
- 在真实场景数据上实现更精确的表面重建，提升视觉质量
- 适合需要高质量三维重建的科研与工业应用
最近，高斯点阵（Gaussian Splatting, GS）在表面重建领域受到广泛关注。然而，现实世界中的3D物体形状复杂多样，现有基于GS的方法仅使用单一类型的点阵基元（如高斯椭圆或高斯椭球）来表示物体表面，这可能难以实现高质量重建。本文首次提出一种新框架，使高斯点阵在表面重建过程中能够融合多种几何基元。具体而言，我们设计了一种组合式点阵策略，支持在高斯点阵流水线中对不同类型基元进行点阵与渲染；同时引入混合基元初始化策略和顶点剪枝机制，以更好地利用不同基元提升表面表征学习能力。大量实验验证了该框架的有效性，展现出优异的表面重建精度。
原文摘要 · Abstract (English)
Recently, Gaussian Splatting (GS) has received a lot of attention in surface reconstruction. However, while 3D objects can be of complex and diverse shapes in the real world, existing GS-based methods only limitedly use a single type of splatting primitive (Gaussian ellipse or Gaussian ellipsoid) to represent object surfaces during their reconstruction. In this paper, we highlight that this can be insufficient for object surfaces to be represented in high quality. Thus, we propose a novel framework that, for the first time, enables Gaussian Splatting to incorporate multiple types of (geometrical) primitives during its surface reconstruction process. Specifically, in our framework, we first propose a compositional splatting strategy, enabling the splatting and rendering of different types of primitives in the Gaussian Splatting pipeline. In addition, we also design our framework with a mixed-primitive-based initialization strategy and a vertex pruning mechanism to further promote its surface representation learning process to be well executed leveraging different types of primitives. Extensive experiments show the efficacy of our framework and its accurate surface reconstruction performance.
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