Modern high-resolution satellite sensors collect optical imagery with ground sampling distances (GSDs) of 30–50 cm, which has sparked a renewed interest in photogrammetric 3D surface reconstruction from satellite data. State-of-the-art reconstruction methods typically generate 2.5D elevation data. Here, we present an approach to recover full 3D surface meshes from multi-view satellite imagery. The proposed method takes as input a coarse initial mesh and refines it by iteratively updating all vertex positions to maximise the photo-consistency between images. Photo-consistency is measured in image space, by transferring texture from one image to another via the surface. We derive the equations to propagate changes in texture similarity through the rational function model (RFM), often also referred to as rational polynomial coefficient (RPC) model. Furthermore, we devise a hierarchical scheme to optimise the surface with gradient descent. In experiments with two different datasets, we show that the refinement improves the initial digital elevation models (DEMs) generated with conventional dense image matching. Moreover, we demonstrate that our method is able to reconstruct true 3D geometry, such as facade structures, if off-nadir views are available.

现代高分辨率卫星传感器以30–50 cm的地面采样距离（GSD）收集光学图像，这引起了人们对从卫星数据进行摄影3D表面重建的新兴趣。最先进的重建方法通常会生成2.5D高程数据。在这里，我们提出了一种从多视图卫星图像恢复完整3D表面网格的方法。所提出的方法将粗糙的初始网格作为输入，并通过迭代更新所有顶点位置以使图像之间的光一致性最大化来对其进行优化。通过将纹理从一个图像通过表面转移到另一个图像，可以测量图像空间中的光一致性。我们通过有理函数模型（RFM）（通常也称为有理多项式系数（RPC）模型）导出方程式，以传播纹理相似性的变化。此外，我们设计了一种分层方案来优化具有梯度下降的表面。在两个不同的数据集的实验中，我们表明改进改进了常规密集图像匹配生成的初始数字高程模型（DEM）。此外，我们证明了我们的方法能够重建真实的3D几何图形，例如立面视图（如果有外底视图的话）。