Sparse2DGS: Geometry-Prioritized Gaussian Splatting for Surface Reconstruction from Sparse Views
This work addresses the problem of surface reconstruction for applications like 3D modeling and computer vision, but it is incremental as it builds on existing Gaussian Splatting and MVS techniques.
The paper tackles surface reconstruction from sparse input views by proposing Sparse2DGS, a Gaussian Splatting method that incorporates geometric-prioritized enhancement schemes to address ill-posed optimization, resulting in more complete and accurate reconstructions with a 2x speed improvement over NeRF-based approaches.
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.