Gaussian Opacity Fields: Efficient and Compact Surface Reconstruction in
  Unbounded Scenes
Zehao Yu Torsten Sattler Andreas Geiger
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.