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

TL;DR

SparseSurf introduces a novel approach for surface reconstruction from sparse views by enhancing geometry-texture alignment and multi-view consistency, leading to improved accuracy and rendering quality.

Contribution

It proposes Stereo Geometry-Texture Alignment and Pseudo-Feature Enhanced Geometry Consistency to address overfitting in sparse-view Gaussian splatting.

Findings

Achieves state-of-the-art results on DTU, BlendedMVS, and Mip-NeRF360 datasets.

Improves surface detail and view synthesis quality in sparse-view scenarios.

Effectively mitigates overfitting caused by limited input views.

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.