AGS-Mesh：基于几何先验的自适应高斯溅射与网格化方法用于智能手机室内房间重建
AGS-Mesh: Adaptive Gaussian Splatting and Meshing with Geometric Priors for Indoor Room Reconstruction Using Smartphones
AI总结：
提出AGS-Mesh方法，利用自适应过滤几何先验优化高斯溅射的深度和法线，结合尺度感知网格化策略，提升室内场景重建精度。
摘要：
几何先验常被用于增强三维重建。由于许多智能手机配备低分辨率深度传感器，且现成的单目几何估计器普遍可用，将几何先验作为正则化信号纳入三维视觉任务已变得常见。然而，对于高度细节化的几何结构，移动设备深度估计的精度通常较差，单目估计器也常存在多视图一致性和精度不佳的问题。在本工作中，我们提出一种针对高斯溅射方法的联合表面深度和法线细化方法，以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和法线估计。在先验估计具有高不确定性或模糊性的区域，我们减轻正则化。我们的过滤策略和优化设计在具有挑战性的室内房间数据集上，对基于三维和二维高斯溅射的方法在网格估计和新视角合成方面均展现出显著改进。此外，我们探索了用于更精细几何提取的替代网格化策略。我们开发了一种受TSDF和基于八叉树的等值面提取启发的尺度感知网格化策略，与其他常用的开源网格化工具相比，它能从高斯模型中恢复更精细的细节。我们的代码发布于 https://xuqianren.github.io/ags_mesh_website/。
英文摘要：
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/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
