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.

翻译：近期基于高斯溅射优化场景几何的研究进展，使得从图像中高效重建精细表面成为可能。然而，当输入视角稀疏时，此类优化容易过拟合，导致重建质量下降。现有方法通过采用扁平化高斯基元以更好地拟合表面几何，并结合深度正则化来缓解有限视角下的几何模糊性，以应对这一挑战。然而，扁平化高斯本身固有的增强各向异性在稀疏视角场景中加剧了过拟合问题，阻碍了准确的表面拟合并降低了新视角合成性能。本文提出SparseSurf方法，能够在保持高质量新视角渲染的同时，重建更精确、更细致的表面。我们的核心思路是引入立体几何-纹理对齐机制，该机制在渲染质量与几何估计之间建立桥梁，从而共同提升表面重建与视角合成的效果。此外，我们提出了一种伪特征增强的几何一致性约束，通过融合训练视角与未见视角来强化多视角几何一致性，有效缓解因稀疏监督导致的过拟合。在DTU、BlendedMVS和Mip-NeRF360数据集上的大量实验表明，本方法实现了最先进的性能。