Surface reconstruction from multi-view images is a core challenge in 3D vision. Recent studies have explored signed distance fields (SDF) within Neural Radiance Fields (NeRF) to achieve high-fidelity surface reconstructions. However, these approaches often suffer from slow training and rendering speeds compared to 3D Gaussian splatting (3DGS). Current state-of-the-art techniques attempt to fuse depth information to extract geometry from 3DGS, but frequently result in incomplete reconstructions and fragmented surfaces. In this paper, we introduce GSurf, a novel end-to-end method for learning a signed distance field directly from Gaussian primitives. The continuous and smooth nature of SDF addresses common issues in the 3DGS family, such as holes resulting from noisy or missing depth data. By using Gaussian splatting for rendering, GSurf avoids the redundant volume rendering typically required in other GS and SDF integrations. Consequently, GSurf achieves faster training and rendering speeds while delivering 3D reconstruction quality comparable to neural implicit surface methods, such as VolSDF and NeuS. Experimental results across various benchmark datasets demonstrate the effectiveness of our method in producing high-fidelity 3D reconstructions.

本研究针对传统三维重建方法在训练和渲染速度慢以及重建表面不完整等问题进行探讨。提出了一种新颖的端到端方法GSurf，能够直接从高斯原语学习有符号距离场，解决了由噪声或缺失深度数据引起的孔洞问题，并且实现了更快的训练和渲染速度，重建质量与当前的神经隐式表面方法相媲美，具有较强的应用潜力。

GSurf：通过直接高斯监督的有符号距离场进行三维重建