Geometric priors are often used to enhance 3D reconstruction. With many smartphones featuring low-resolution depth sensors and the prevalence of off-the-shelf monocular geometry estimators, incorporating geometric priors as regularization signals has become common in 3D vision tasks. However, the accuracy of depth estimates from mobile devices is typically poor for highly detailed geometry, and monocular estimators often suffer from poor multi-view consistency and precision. In this work, we propose an approach for joint surface depth and normal refinement of Gaussian Splatting methods for accurate 3D reconstruction of indoor scenes. We develop supervision strategies that adaptively filters low-quality depth and normal estimates by comparing the consistency of the priors during optimization. We mitigate regularization in regions where prior estimates have high uncertainty or ambiguities. Our filtering strategy and optimization design demonstrate significant improvements in both mesh estimation and novel-view synthesis for both 3D and 2D Gaussian Splatting-based methods on challenging indoor room datasets. Furthermore, we explore the use of alternative meshing strategies for finer geometry extraction. We develop a scale-aware meshing strategy inspired by TSDF and octree-based isosurface extraction, which recovers finer details from Gaussian models compared to other commonly used open-source meshing tools. Our code is released in https://xuqianren.github.io/ags_mesh_website/.

翻译：几何先验常被用于增强三维重建。随着众多智能手机配备低分辨率深度传感器以及现成的单目几何估计器日益普及，将几何先验作为正则化信号融入三维视觉任务已成为常见做法。然而，对于高度精细的几何结构，移动设备获取的深度估计精度通常较差，且单目估计器常存在多视角一致性与精确性不足的问题。本研究提出一种针对高斯溅射方法的联合表面深度与法向优化方案，旨在实现室内场景的精确三维重建。我们开发了监督策略，通过在优化过程中比较先验的一致性，自适应地过滤低质量的深度与法向估计。在先验估计具有高不确定性或模糊性的区域，我们减轻了正则化强度。在具有挑战性的室内房间数据集上，我们的过滤策略与优化设计显著提升了基于三维与二维高斯溅射方法的网格估计质量与新视角合成效果。此外，我们探索了替代性网格化策略以实现更精细的几何提取。受TSDF与基于八叉树的等值面提取方法启发，我们提出了一种尺度感知的网格化策略，相较于其他常用开源网格化工具，该策略能从高斯模型中恢复更精细的几何细节。我们的代码发布于 https://xuqianren.github.io/ags_mesh_website/。