AGS-Mesh: Adaptive Gaussian Splatting and Meshing with Geometric Priors for Indoor Room Reconstruction Using Smartphones
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.
几何先验通常用于增强 3D 重建。随着许多智能手机配备低分辨率深度传感器，以及现成单目几何估计器的普及，在 3D 视觉任务中将几何先验作为正则化信号进行整合已变得十分常见。然而，移动设备生成的深度估计在高细节几何方面通常精度较低，而单目估计器则往往缺乏多视图一致性和精确度。 在本研究中，我们提出了一种 联合表面深度和法向量细化的高斯点云方法，用于精确重建室内场景的 3D 几何。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和法向量估计。在先验估计具有较高不确定性或存在歧义的区域，我们减少正则化的影响。我们的过滤策略和优化设计在 3D 和 2D 高斯点云方法基础上，对挑战性室内场景数据集的网格估计和新视图合成表现出了显著改进。 此外，我们还探索了用于提取更精细几何的替代网格生成策略。我们开发了一种基于 TSDF（截断符号距离函数）和八叉树等值面提取 的尺度感知网格生成策略，相比其他常用的开源网格生成工具，该策略能够从高斯模型中恢复更精细的几何细节。