PolyGNN
Polyhedron-based graph neural network for 3D building reconstruction from point clouds
Zhaiyu Chen (Technische Universität München)
Yilei Shi (Technische Universität München)
L. Nan (TU Delft - Urban Data Science)
Zhitong Xiong (Technische Universität München)
Xiao Xiang Zhu (Technische Universität München, Munich Center for Machine Learning (MCML))
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Abstract
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 and compact reconstruction. To effectively represent arbitrary-shaped polyhedra in the neural network, we propose a skeleton-based sampling strategy to generate polyhedron-wise queries. These queries are then incorporated with inter-polyhedron adjacency to enhance the classification. PolyGNN is end-to-end optimizable and is designed to accommodate variable-size input points, polyhedra, and queries with an index-driven batching technique. To address the abstraction gap between existing city-building models and the underlying instances, and provide a fair evaluation of the proposed method, we develop our method on a large-scale synthetic dataset with well-defined ground truths of polyhedral 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.
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