  Building 3D reconstruction from remote sensing images has a wide range of
applications in smart cities, photogrammetry and other fields. Methods for
automatic 3D urban building modeling typically employ multi-view images as
input to algorithms to recover point clouds and 3D models of buildings.
However, such models rely heavily on multi-view images of buildings, which are
time-intensive and limit the applicability and practicality of the models. To
solve these issues, we focus on designing an efficient DSM estimation-driven
reconstruction framework (Building3D), which aims to reconstruct 3D building
models from the input single-view remote sensing image. First, we propose a
Semantic Flow Field-guided DSM Estimation (SFFDE) network, which utilizes the
proposed concept of elevation semantic flow to achieve the registration of
local and global features. Specifically, in order to make the network semantics
globally aware, we propose an Elevation Semantic Globalization (ESG) module to
realize the semantic globalization of instances. Further, in order to alleviate
the semantic span of global features and original local features, we propose a
Local-to-Global Elevation Semantic Registration (L2G-ESR) module based on
elevation semantic flow. Our Building3D is rooted in the SFFDE network for
building elevation prediction, synchronized with a building extraction network
for building masks, and then sequentially performs point cloud reconstruction,
surface reconstruction (or CityGML model reconstruction). On this basis, our
Building3D can optionally generate CityGML models or surface mesh models of the
buildings. Extensive experiments on ISPRS Vaihingen and DFC2019 datasets on the
DSM estimation task show that our SFFDE significantly improves upon
state-of-the-arts. Furthermore, our Building3D achieves impressive results in
the 3D point cloud and 3D model reconstruction process.
