Recently, 3D Gaussian Splatting (3DGS) has garnered significant attention. However, the unstructured nature of 3DGS poses challenges for large-scale surface reconstruction from aerial images. To address this gap, we propose the first large-scale surface reconstruction method for multi-view stereo (MVS) aerial images based on 3DGS, named Aerial Gaussian Splatting (AGS). Initially, we introduce a data chunking method tailored for large-scale aerial imagery, making the modern 3DGS technology feasible for surface reconstruction over extensive scenes. Additionally, we integrate the Ray-Gaussian Intersection method to obtain normal and depth information, facilitating geometric constraints. Finally, we introduce a multi-view geometric consistency constraint to enhance global geometric consistency and improve reconstruction accuracy. Our experiments on multiple datasets demonstrate for the first time that the GS-based technique can match traditional aerial MVS methods on geometric accuracy, and beat state-of-the-art GS-based methods on geometry and rendering quality.

本研究针对传统3D高斯点云在航拍图像大规模表面重建中的不足，提出了一种名为“航拍高斯点云”（AGS）的新方法。该方法通过数据分块和光线-高斯交集技术，结合多视角几何一致性约束，显著提高了重建的几何一致性与精度，实验表明其在几何准确性上与传统方法相匹配。

基于3D高斯点云的航拍图像大规模3D表面重建