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.
Our code will be made open-source upon paper acceptance.

通过直接将法线与其他几何参数耦合，提出了一种深度法线正则化器以完全更新几何参数，并引入置信度项以减轻多视图法线预测的不一致性，同时通过密度化和分割策略对 3D Gaussians 的大小和分布进行正则化，实验结果表明该方法在更快的训练速度和 100+ FPS 渲染时获得更好的重建质量和竞争性外观质量。