  Modern high-resolution satellite sensors collect optical imagery with ground
sampling distances (GSDs) of 30-50cm, 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 maximize 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 optimize 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.
