  In this paper, we propose a model-driven method that reconstructs LoD-2
building models following a "decomposition-optimization-fitting" paradigm. The
proposed method starts building detection results through a deep learning-based
detector and vectorizes individual segments into polygons using a "three-step"
polygon extraction method, followed by a novel grid-based decomposition method
that decomposes the complex and irregularly shaped building polygons to tightly
combined elementary building rectangles ready to fit elementary building
models. We have optionally introduced OpenStreetMap (OSM) and Graph-Cut (GC)
labeling to further refine the orientation of 2D building rectangle. The 3D
modeling step takes building-specific parameters such as hip lines, as well as
non-rigid and regularized transformations to optimize the flexibility for using
a minimal set of elementary models. Finally, roof type of building models s
refined and adjacent building models in one building segment are merged into
the complex polygonal model. Our proposed method has addressed a few technical
caveats over existing methods, resulting in practically high-quality results,
based on our evaluation and comparative study on a diverse set of experimental
datasets of cities with different urban patterns.
