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 incorporating an implicit signed
distance field (SDF) within 3D Gaussians to enable them to be aligned and
jointly optimized. First, we introduce a differentiable SDF-to-opacity
transformation function that converts SDF values into corresponding Gaussians'
opacities. This function connects the SDF and 3D Gaussians, allowing for
unified optimization and enforcing surface constraints on the 3D Gaussians.
During learning, optimizing the 3D Gaussians provides supervisory signals for
SDF learning, enabling the reconstruction of intricate details. However, this
only provides 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 those derived from 3D
Gaussians. This consistency regularization introduces supervisory signals to
locations not covered by discrete 3D Gaussians, effectively eliminating
redundant surfaces outside the Gaussian sampling range. Our extensive
experimental results demonstrate that our 3DGSR method 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. The code will be available at
this https URL

该研究论文介绍了一种包括隐式曲面重建、三维高斯光照插值以及体积渲染等方法组合的 3D 曲面重建技术，实现了高质量的三维曲面重建并保持了高效率和渲染质量。

3DGSR：基于 3D 高斯扩展通过隐式表面重建

3DGSR: Implicit Surface Reconstruction with 3D Gaussian Splatting

This paper studies implicit surface reconstruction leveraging differentiable
ray casting. Previous works such as IDR and NeuS overlook the spatial context
in 3D space when predicting and rendering the surface, thereby may fail to
capture sharp local topologies such as small holes and structures. To mitigate
the limitation, we propose a flexible neural implicit representation leveraging
hierarchical voxel grids, namely Neural Deformable Anchor (NeuDA), for
high-fidelity surface reconstruction. NeuDA maintains the hierarchical anchor
grids where each vertex stores a 3D position (or anchor) instead of the direct
embedding (or feature). We optimize the anchor grids such that different local
geometry structures can be adaptively encoded. Besides, we dig into the
frequency encoding strategies and introduce a simple hierarchical positional
encoding method for the hierarchical anchor structure to flexibly exploit the
properties of high-frequency and low-frequency geometry and appearance.
Experiments on both the DTU and BlendedMVS datasets demonstrate that NeuDA can
produce promising mesh surfaces.

本文研究利用可微分射线投射进行隐式表面重建。我们提出了一种高保真表面重建的柔性神经隐式表示方法 NeuDA，利用分层体素网格维护分层锚点网格。此外，我们还深入探讨了位置编码策略，并引入了一种简单的分层位置编码方法，以灵活利用高频和低频几何与外观的特性。实验证明，NeuDA 可以产生有前途的网格表面。