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

• URL: http://arxiv.org/abs/2307.08636v2
• Date: Fri, 11 Oct 2024 21:33:28 GMT
• Title: PolyGNN: Polyhedron-based Graph Neural Network for 3D Building Reconstruction from Point Clouds
• Authors: Zhaiyu Chen, Yilei Shi, Liangliang Nan, Zhitong Xiong, Xiao Xiang Zhu,
• Abstract summary: PolyGNN is a graph neural network for building reconstruction point clouds. It learns to assemble primitives obtained by polyhedral decomposition. We conduct a transferability analysis across cities and on real-world point clouds.
• Score: 22.18061879431175
• License:
• 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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