  While three-dimensional (3D) building models play an increasingly pivotal
role in many real-world applications, obtaining a compact representation of
buildings remains an open problem. In this paper, we present a novel framework
for reconstructing compact, watertight, polygonal building models from point
clouds. Our framework comprises three components: (a) a cell complex is
generated via adaptive space partitioning that provides a polyhedral embedding
as the candidate set; (b) an implicit field is learned by a deep neural network
that facilitates building occupancy estimation; (c) a Markov random field is
formulated to extract the outer surface of a building via combinatorial
optimization. We evaluate and compare our method with state-of-the-art methods
in generic reconstruction, model-based reconstruction, geometry simplification,
and primitive assembly. Experiments on both synthetic and real-world point
clouds have demonstrated that, with our neural-guided strategy, high-quality
building models can be obtained with significant advantages in fidelity,
compactness, and computational efficiency. Our method also shows robustness to
noise and insufficient measurements, and it can directly generalize from
synthetic scans to real-world measurements. The source code of this work is
freely available at https://github.com/chenzhaiyu/points2poly.
