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

• URL: http://arxiv.org/abs/2607.03765v1
• Date: Sat, 04 Jul 2026 08:33:40 GMT
• Title: Sparse-View Surface Reconstruction using Gaussian Splatting through High-Confidence Depth Propagation with Normal Priors
• Abstract summary: We propose a novel 3DGS-based method for high-fidelity surface reconstruction from sparse views.<n>Our key insight is to introduce a normal-guided depth propagation approach.<n>Our method outperforms the state-of-the-art methods in sparse view surface reconstruction.
• Score: 88.6801530914898
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 3D reconstruction from sparse views is a challenging task in 3D computer vision. Recent studies on 3D Gaussian Splatting (3DGS) have achieved remarkable results with sparse views in novel view synthesis, yet reconstructing high-quality geometric surfaces from sparse views remains a challenge, due to the limited geometry clues and the discreteness of Gaussians. In this paper, we propose a novel 3DGS-based method for high-fidelity surface reconstruction from sparse views. Our key insight is to introduce a normal-guided depth propagation approach, which can extend depth information from high-confidence regions to constrain the depth in low-confidence areas. Additionally, we propose an abnormal depth edge-aware regularization to address depth discontinuities caused by the discreteness of Gaussians. Extensive experiments on DTU and Tanks-and-Temples datasets demonstrate that our method outperforms the state-of-the-art methods in sparse view surface reconstruction. Project page: https://hanl2010.github.io/DP-GS.

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