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.
从多视角图像中进行表面重建是 3D 视觉领域的核心挑战。近年来的研究通过在神经辐射场（Neural Radiance Fields, NeRF）中利用有符号距离场（Signed Distance Fields, SDF），实现了高保真的表面重建。然而，这些方法的训练和渲染速度通常比 3D 高斯投影（3D Gaussian Splatting, 3DGS）慢得多。当前最先进的技术尝试将深度信息融合到 3DGS 中以提取几何，但经常导致重建不完整或表面破碎的问题。 本文提出了一种名为 GSurf 的新颖端到端方法，直接从高斯基元学习有符号距离场（SDF）。SDF 的连续和平滑特性有效解决了 3DGS 方法中常见的问题，例如由于噪声或深度数据缺失导致的孔洞。通过使用高斯投影进行渲染，GSurf 避免了其他 GS 和 SDF 集成方法中常见的冗余体积渲染。由此，GSurf 实现了更快的训练和渲染速度，同时在 3D 重建质量上可与神经隐式表面方法（如 VolSDF 和 NeuS）相媲美。 在多个基准数据集上的实验结果表明，GSurf 能够高效生成高保真的 3D 重建，验证了其方法的有效性。