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.
最近，3D高斯飞溅（3DGS）在新视角合成方面展示了令人印象深刻的结果，同时允许实时渲染高分辨率图像。然而，利用3D高斯进行表面重建因其显式且断开的性质而面临重大挑战。在这项工作中，我们提出了高斯不透明度场（GOF），一种用于无界场景中高效、高质量和紧凑表面重建的新方法。我们的GOF基于基于光线追踪的3D高斯体积渲染，使直接从3D高斯提取几何体成为可能，通过识别其水平集，无需依赖于之前工作中的泊松重建或TSDF融合。我们将高斯的表面法线近似为光线与高斯交点平面的法线，使得可以应用显著增强几何体的正则化。此外，我们开发了一种高效的几何体提取方法，使用行进四面体，其中四面体网格由3D高斯诱导并因此适应场景的复杂性。我们的评估显示，GOF在表面重建和新视角合成方面超越了现有的基于3DGS的方法。此外，它在质量和速度方面与神经隐式方法相比有优势，甚至表现更好。