Recently, 3D Gaussian Splatting (3DGS) has demonstrated impressive novel view
synthesis results, while allowing the rendering of high-resolution images in
real-time. However, leveraging 3D Gaussians for surface reconstruction poses
significant challenges due to the explicit and disconnected nature of 3D
Gaussians. In this work, we present Gaussian Opacity Fields (GOF), a novel
approach for efficient, high-quality, and compact surface reconstruction in
unbounded scenes. Our GOF is derived from ray-tracing-based volume rendering of
3D Gaussians, enabling direct geometry extraction from 3D Gaussians by
identifying its levelset, without resorting to Poisson reconstruction or TSDF
fusion as in previous work. We approximate the surface normal of Gaussians as
the normal of the ray-Gaussian intersection plane, enabling the application of
regularization that significantly enhances geometry. Furthermore, we develop an
efficient geometry extraction method utilizing marching tetrahedra, where the
tetrahedral grids are induced from 3D Gaussians and thus adapt to the scene's
complexity. Our evaluations reveal that GOF surpasses existing 3DGS-based
methods in surface reconstruction and novel view synthesis. Further, it
compares favorably to, or even outperforms, neural implicit methods in both
quality and speed.

通过使用高斯不透明度场 (GOF)，本研究提出了一种高效、高质量、紧凑的无界场景表面重建方法，该方法借助基于光线追踪的 3D 高斯体积渲染，直接提取来自高斯体积的几何信息，并通过正则化来改善几何表达，同时利用阶梯四面体提取方法适应复杂场景，实现了超越现有方法的表面重建和新视角合成效果，同时在质量和速度方面与神经隐式方法相比有明显优势。