用稀疏视角重建三维表面，速度更快且精度更高
Sparse2DGS: Geometry-Prioritized Gaussian Splatting for Surface Reconstruction from Sparse Views
- 基于MVS初始化，引入几何先验增强稀疏视图下的优化
- 在稀疏视图下实现完整准确的三维重建，优于现有方法
- 适合需要快速高精度重建的稀疏视角场景
我们提出一种基于稀疏输入视角的高斯点阵表面重建方法。以往依赖密集视角的方法在结构光点稀疏时难以初始化；虽有基于学习的多视角立体（MVS）可生成稠密3D点，但直接结合高斯点阵会导致稀疏视角几何优化病态问题。为此，我们提出Sparse2DGS，一种由MVS初始化的高斯点阵重建流程，核心思想是引入几何先验增强策略，在病态条件下实现直接且鲁棒的几何学习。Sparse2DGS在性能上显著优于现有方法，且相比基于NeRF的微调方法快2倍。
原文摘要 · Abstract (English)
We present a Gaussian Splatting method for surface reconstruction using sparse input views. Previous methods relying on dense views struggle with extremely sparse Structure-from-Motion points for initialization. While learning-based Multi-view Stereo (MVS) provides dense 3D points, directly combining it with Gaussian Splatting leads to suboptimal results due to the ill-posed nature of sparse-view geometric optimization. We propose Sparse2DGS, an MVS-initialized Gaussian Splatting pipeline for complete and accurate reconstruction. Our key insight is to incorporate the geometric-prioritized enhancement schemes, allowing for direct and robust geometric learning under ill-posed conditions. Sparse2DGS outperforms existing methods by notable margins while being ${2}\times$ faster than the NeRF-based fine-tuning approach.
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