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arXiv · 2307.08636

PolyGNN: Polyhedron-based Graph Neural Network for 3D Building Reconstruction from Point Clouds

Zhaiyu Chen·Yilei Shi·Liangliang Nan·Zhitong Xiong·Xiao Xiang Zhu
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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. The source code and data are available at https://github.com/chenzhaiyu/polygnn.

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Zhaiyu Chen, Yilei Shi, Liangliang Nan, Zhitong Xiong, Xiao Xiang Zhu. 2024-10-11. PolyGNN: Polyhedron-based Graph Neural Network for 3D Building Reconstruction from Point Clouds. https://doi.org/10.1016/j.isprsjprs.2024.09.031

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