State-of-the-art novel view synthesis methods such as 3D Gaussian Splatting (3DGS) achieve remarkable visual quality. While 3DGS and its variants can be rendered efficiently using rasterization, many tasks require access to the underlying 3D surface, which remains challenging to extract due to the sparse and explicit nature of this representation. In this paper, we introduce G2SDF, a novel approach that addresses this limitation by integrating a neural implicit Signed Distance Field (SDF) into the Gaussian Splatting framework. Our method links the opacity values of Gaussians with their distances to the surface, ensuring a closer alignment of Gaussians with the scene surface. To extend this approach to unbounded scenes at varying scales, we propose a normalization function that maps any range to a fixed interval. To further enhance reconstruction quality, we leverage an off-the-shelf depth estimator as pseudo ground truth during Gaussian Splatting optimization. By establishing a differentiable connection between the explicit Gaussians and the implicit SDF, our approach enables high-quality surface reconstruction and rendering. Experimental results on several real-world datasets demonstrate that G2SDF achieves superior reconstruction quality than prior works while maintaining the efficiency of 3DGS.

本研究解决了从稀疏显式高斯表示中提取3D表面的挑战，提出了一种将神经隐式已签距场（SDF）与高斯点喷射框架相结合的新方法G2SDF。该方法通过链接高斯的不透明度值与其到表面距离，实现了高质量的表面重建与渲染，并在多个真实数据集上展示了优于以往方法的重建质量。

G2SDF：基于显式高斯的隐式SDF表面重建