We propose a novel point-based representation, Gaussian surfels, to combine
the advantages of the flexible optimization procedure in 3D Gaussian points and
the surface alignment property of surfels. This is achieved by directly setting
the z-scale of 3D Gaussian points to 0, effectively flattening the original 3D
ellipsoid into a 2D ellipse. Such a design provides clear guidance to the
optimizer. By treating the local z-axis as the normal direction, it greatly
improves optimization stability and surface alignment. While the derivatives to
the local z-axis computed from the covariance matrix are zero in this setting,
we design a self-supervised normal-depth consistency loss to remedy this issue.
Monocular normal priors and foreground masks are incorporated to enhance the
quality of the reconstruction, mitigating issues related to highlights and
background. We propose a volumetric cutting method to aggregate the information
of Gaussian surfels so as to remove erroneous points in depth maps generated by
alpha blending. Finally, we apply screened Poisson reconstruction method to the
fused depth maps to extract the surface mesh. Experimental results show that
our method demonstrates superior performance in surface reconstruction compared
to state-of-the-art neural volume rendering and point-based rendering methods.

我们提出了一种新颖的基于点的表示方法，高斯 surfels，通过直接将 3D 高斯点的 z 缩放设置为 0，将原始的 3D 椭球形状变为 2D 椭圆，进而提供了对优化过程的明确指导，从而在优化稳定性和表面对齐方面取得了显著的改进。我们还设计了一个自监督的法线深度一致性损失，以解决由于该设置使得从协方差矩阵计算得到的法线方向导数为零的问题。通过引入单目法线和前景蒙版，我们提高了重建质量，并缓解了与高光和背景相关的问题。此外，我们提出了一种用于聚合高斯 surfels 信息以去除通过 alpha 混合生成的深度图中的错误点的容积切割方法。最后，我们应用屏蔽泊松重建方法提取了表面网格。实验证明，与最先进的神经体积渲染和基于点的渲染方法相比，我们的方法在表面重建方面表现出卓越的性能。