Sparse2DGS：面向稀疏视角表面重建的几何优先高斯泼溅
Sparse2DGS: Geometry-Prioritized Gaussian Splatting for Surface Reconstruction from Sparse Views
AI总结：
针对稀疏视角下表面重建的病态优化问题，提出MVS初始化的Sparse2DGS管线，通过引入几何优先增强方案实现鲁棒几何学习，在精度上显著超越现有方法且速度提升2倍。
摘要：
我们提出一种使用稀疏输入视角进行表面重建的Gaussian Splatting方法。以往依赖密集视角的方法在初始化时难以处理极度稀疏的Structure-from-Motion点。尽管基于学习的Multi-view Stereo (MVS)能提供密集3D点，但由于稀疏视角几何优化的病态性质，将其直接与Gaussian Splatting结合会导致次优结果。我们提出Sparse2DGS，一种由MVS初始化的Gaussian Splatting管线，以实现完整且准确的重建。我们的核心见解是引入几何优先的增强方案，从而在病态条件下实现直接且鲁棒的几何学习。Sparse2DGS以显著优势超越现有方法，同时比基于NeRF的微调方法快${2}\ imes$。
英文摘要：
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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