Although 3D Gaussian Splatting has been widely studied because of its realistic and efficient novel-view synthesis, it is still challenging to extract a high-quality surface from the point-based representation. Previous works improve the surface by incorporating geometric priors from the off-the-shelf normal estimator. However, there are two main limitations: 1) Supervising normal rendered from 3D Gaussians updates only the rotation parameter while neglecting other geometric parameters; 2) The inconsistency of predicted normal maps across multiple views may lead to severe reconstruction artifacts. In this paper, we propose a Depth-Normal regularizer that directly couples normal with other geometric parameters, leading to full updates of the geometric parameters from normal regularization. We further propose a confidence term to mitigate inconsistencies of normal predictions across multiple views. Moreover, we also introduce a densification and splitting strategy to regularize the size and distribution of 3D Gaussians for more accurate surface modeling. Compared with Gaussian-based baselines, experiments show that our approach obtains better reconstruction quality and maintains competitive appearance quality at faster training speed and 100+ FPS rendering.
虽然由于其现实且高效的新视角合成，3D高斯涂抹已被广泛研究，但从基于点的表示中提取高质量表面仍然是一个挑战。先前的工作通过整合现成的法线估计器中的几何先验来改善表面质量。然而，存在两个主要的局限性：1) 对从3D高斯渲染的法线进行监督只更新旋转参数，而忽略了其他几何参数；2) 多视图中预测的法线图的不一致可能导致严重的重建缺陷。在本文中，我们提出了一个深度-法线正则化器，该正则化器直接将法线与其他几何参数相耦合，从而实现了从法线正则化中对几何参数的完全更新。我们进一步提出了一个置信度项，以减轻多视图中法线预测的不一致性。此外，我们还引入了一种密化和分裂策略，以规范3D高斯的大小和分布，从而实现更精确的表面建模。与基于高斯的基线相比，实验表明我们的方法获得了更好的重建质量，并且在更快的训练速度和100+ FPS的渲染速度下保持了竞争性的外观质量。