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.
最先进的新视角合成方法，如 3D 高斯投影（3D Gaussian Splatting, 3DGS），在视觉质量上表现出色。虽然 3DGS 及其变体能够通过光栅化高效渲染，但许多任务需要访问底层 3D 表面，而由于其稀疏且显式的表示形式，这一问题仍然具有挑战性。 本文提出了一种新方法 G2SDF，通过将神经隐式有符号距离场（Signed Distance Field, SDF）集成到高斯投影框架中，解决了这一限制。我们的方法将高斯的不透明度值与其到表面的距离关联起来，确保高斯与场景表面更加紧密地对齐。为了扩展到不同尺度的无限场景，我们提出了一种归一化函数，将任意范围映射到固定区间。此外，为了进一步提高重建质量，我们在高斯投影优化过程中利用现成的深度估计器作为伪地面实况。 通过在显式高斯与隐式 SDF 之间建立可微分连接，G2SDF 实现了高质量的表面重建和渲染。在多个真实世界数据集上的实验结果表明，G2SDF 在保持 3DGS 高效性的同时，比现有方法实现了更优越的重建质量。