Sparse-View Surface Reconstruction using Gaussian Splatting through High-Confidence Depth Propagation with Normal Priors

3D reconstruction from sparse views is a challenging task in3D computer vision. Recent studies on 3D Gaussian Splatting (3DGS)have achieved remarkable results with sparse views in novel view syn-thesis, yet reconstructing high-quality geometric surfaces from sparseviews remains a challenge, due to the limited geometry clues and thediscreteness of Gaussians. In this paper, we propose a novel 3DGS-basedmethod for high-fidelity surface reconstruction from sparse views. Ourkey insight is to introduce a normal-guided depth propagation approach,which can extend depth information from high-confidence regions to con-strain the depth in low-confidence areas. Additionally, we propose an ab-normal depth edge-aware regularization to address depth discontinuitiescaused by the discreteness of Gaussians. Extensive experiments on DTUand Tanks-and-Temples datasets demonstrate that our method outper-forms the state-of-the-art methods in sparse view surface reconstruction.Project page: https://hanl2010.github.io/DP-GS.