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.

翻译：我们提出了一种新颖的基于点的表示方法——高斯曲面元，旨在结合三维高斯点在灵活优化过程中的优势与曲面元的表面对齐特性。通过直接将三维高斯点的z尺度设置为零，我们将原始三维椭球体展平为二维椭圆。这种设计为优化器提供了清晰的方向指引。通过将局部z轴视为法线方向，该方法显著提升了优化稳定性与表面对齐效果。尽管在此设定下，由协方差矩阵计算的局部z轴导数为零，但我们设计了一种自监督法线-深度一致性损失函数来弥补这一不足。此外，我们引入单目法线先验与前景掩码来增强重建质量，减轻高光与背景相关问题。为了聚合高斯曲面元的信息，我们提出了一种体切割方法，以去除通过alpha混合生成的深度图中的错误点。最后，我们对融合后的深度图应用筛选泊松重建方法，提取表面网格。实验结果表明，与最先进的神经体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更优的性能。