PolyGNN: Polyhedron-based Graph Neural Network for 3D Building
  Reconstruction from Point Clouds
Zhaiyu Chen Yilei Shi Liangliang Nan Zhitong Xiong Xiao Xiang Zhu
We present PolyGNN, a polyhedron-based graph neural network for 3D building
reconstruction from point clouds. PolyGNN learns to assemble primitives
obtained by polyhedral decomposition via graph node classification, achieving a
watertight, compact, and weakly semantic reconstruction. To effectively
represent arbitrary-shaped polyhedra in the neural network, we propose three
different sampling strategies to select representative points as
polyhedron-wise queries, enabling efficient occupancy inference. Furthermore,
we incorporate the inter-polyhedron adjacency to enhance the classification of
the graph nodes. We also observe that existing city-building models are
abstractions of the underlying instances. To address this abstraction gap and
provide a fair evaluation of the proposed method, we develop our method on a
large-scale synthetic dataset covering 500k+ buildings with well-defined ground
truths of polyhedral class labels. We further conduct a transferability
analysis across cities and on real-world point clouds. Both qualitative and
quantitative results demonstrate the effectiveness of our method, particularly
its efficiency for large-scale reconstructions. The source code and data of our
work are available at https://github.com/chenzhaiyu/polygnn.