用航空影像实现大场景3D高精度重建，效果媲美传统方法
3D Gaussian Splatting for Large-scale Surface Reconstruction from Aerial Images
- 针对大场景设计数据分块策略，让3D高斯点云在航空影像中可行
- 融合射线-高斯相交计算深度与法向，提升几何精度
- 多视角几何一致性约束，适合遥感、测绘等大场景应用
最近，3D高斯点云（3DGS）在小规模3D表面重建中表现出色。然而，将其扩展到大场景仍面临重大挑战。为此，我们提出一种基于3DGS的大规模地表重建新方法——航空高斯点云（AGS），利用航空多视图立体（MVS）影像。首先，我们设计了一种专用于大规模航空影像的数据分块方法，使3DGS在广域地表重建中成为可能。其次，将射线-高斯相交方法集成至3DGS中，以获取深度和法向信息。最后，引入多视图几何一致性约束，增强不同视角间的几何一致性。我们在多个数据集上的实验首次证明，3DGS-based方法可在航空大场景地表重建中达到传统航空MVS方法的几何精度，且在几何与渲染质量上均优于现有最先进的基于高斯点云的方法。
原文摘要 · Abstract (English)
Recently, 3D Gaussian Splatting (3DGS) has demonstrated excellent ability in small-scale 3D surface reconstruction. However, extending 3DGS to large-scale scenes remains a significant challenge. To address this gap, we propose a novel 3DGS-based method for large-scale surface reconstruction using aerial multi-view stereo (MVS) images, named Aerial Gaussian Splatting (AGS). First, we introduce a data chunking method tailored for large-scale aerial images, making 3DGS feasible for surface reconstruction over extensive scenes. Second, we integrate the Ray-Gaussian Intersection method into 3DGS to obtain depth and normal information. Finally, we implement multi-view geometric consistency constraints to enhance the geometric consistency across different views. Our experiments on multiple datasets demonstrate, for the first time, the 3DGS-based method can match conventional aerial MVS methods on geometric accuracy in aerial large-scale surface reconstruction, and our method also beats state-of-the-art GS-based methods both on geometry and rendering quality.
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