In this paper, we present an implicit surface reconstruction method with 3D Gaussian Splatting (3DGS), namely 3DGSR, that allows for accurate 3D reconstruction with intricate details while inheriting the high efficiency and rendering quality of 3DGS. The key insight is to incorporate an implicit signed distance field (SDF) within 3D Gaussians for surface modeling, and to enable the alignment and joint optimization of both SDF and 3D Gaussians. To achieve this, we design coupling strategies that align and associate the SDF with 3D Gaussians, allowing for unified optimization and enforcing surface constraints on the 3D Gaussians. With alignment, optimizing the 3D Gaussians provides supervisory signals for SDF learning, enabling the reconstruction of intricate details. However, this only offers sparse supervisory signals to the SDF at locations occupied by Gaussians, which is insufficient for learning a continuous SDF. Then, to address this limitation, we incorporate volumetric rendering and align the rendered geometric attributes (depth, normal) with that derived from 3DGS. In sum, these two designs allow SDF and 3DGS to be aligned, jointly optimized, and mutually boosted. Our extensive experimental results demonstrate that our 3DGSR enables high-quality 3D surface reconstruction while preserving the efficiency and rendering quality of 3DGS. Besides, our method competes favorably with leading surface reconstruction techniques while offering a more efficient learning process and much better rendering qualities.

中文翻译：

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3DGSR：使用 3D 高斯展开进行隐式表面重建


在本文中，我们提出了一种使用 3D 高斯展开 （3DGS） 的隐式表面重建方法，即 3DGSR，该方法允许具有复杂细节的精确 3D 重建，同时继承了 3DGS 的高效率和渲染质量。关键的见解是在 3D 高斯中加入隐式有符号距离场 （SDF） 以进行表面建模，并实现 SDF 和 3D 高斯的对齐和联合优化。为了实现这一目标，我们设计了耦合策略，将 SDF 与 3D Gaussians对齐和关联，从而允许在 3D Gaussians上进行统一优化和执行表面约束。通过对齐，优化 3D Gaussian 为 SDF 学习提供监督信号，从而能够重建复杂的细节。然而，这只在高斯人占据的位置向 SDF 提供稀疏的监控信号，这对于学习连续的 SDF 来说是不够的。然后，为了解决这一限制，我们合并了体积渲染，并将渲染的几何属性（深度、法线）与从 3DGS 派生的几何属性对齐。总之，这两种设计允许 SDF 和 3DGS 保持一致、共同优化和相互促进。我们大量的实验结果表明，我们的 3DGSR 能够实现高质量的 3D 表面重建，同时保持 3DGS 的效率和渲染质量。此外，我们的方法与领先的表面重建技术竞争，同时提供更高效的学习过程和更好的渲染质量。