SparseSurf: Sparse-View 3D Gaussian Splatting for Surface Reconstruction

• URL: http://arxiv.org/abs/2511.14633v1
• Date: Tue, 18 Nov 2025 16:24:37 GMT
• Title: SparseSurf: Sparse-View 3D Gaussian Splatting for Surface Reconstruction
• Authors: Meiying Gu, Jiawei Zhang, Jiahe Li, Xiaohan Yu, Haonan Luo, Jin Zheng, Xiao Bai,
• Abstract summary: We propose net, a method that reconstructs more accurate and detailed surfaces while preserving high-quality novel view rendering.<n>Our key insight is to introduce Stereo Geometry-Texture Alignment, which bridges rendering quality and geometry estimation.<n>In addition, we present a Pseudo-Feature Enhanced Geometry Consistency that enforces multi-view geometric consistency.
• Score: 26.59203606048875
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in optimizing Gaussian Splatting for scene geometry have enabled efficient reconstruction of detailed surfaces from images. However, when input views are sparse, such optimization is prone to overfitting, leading to suboptimal reconstruction quality. Existing approaches address this challenge by employing flattened Gaussian primitives to better fit surface geometry, combined with depth regularization to alleviate geometric ambiguities under limited viewpoints. Nevertheless, the increased anisotropy inherent in flattened Gaussians exacerbates overfitting in sparse-view scenarios, hindering accurate surface fitting and degrading novel view synthesis performance. In this paper, we propose \net{}, a method that reconstructs more accurate and detailed surfaces while preserving high-quality novel view rendering. Our key insight is to introduce Stereo Geometry-Texture Alignment, which bridges rendering quality and geometry estimation, thereby jointly enhancing both surface reconstruction and view synthesis. In addition, we present a Pseudo-Feature Enhanced Geometry Consistency that enforces multi-view geometric consistency by incorporating both training and unseen views, effectively mitigating overfitting caused by sparse supervision. Extensive experiments on the DTU, BlendedMVS, and Mip-NeRF360 datasets demonstrate that our method achieves the state-of-the-art performance.

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