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Graph machine learning using 3D topological models

Jabi, Wassim and Alymani, Abdulrahman 2020. Graph machine learning using 3D topological models. Presented at: SimAUD 2020 Conference, Online, 25-28 May 2020.

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Classification of urban and architectural works using machine learning techniques have typically focused on 2D pixel-based image recognition. In this paper we present a novel proof-of-concept workflow that enables a machine learning computer system to learn to classify 3D conceptual models based on topological graphs rather than 2D images. The system leverages two main technologies. The first is a custom designed software library that enhances the representation of 3D models through non-manifold topology and embedded semantic information. The second is an end-to-end deep graph convolutional network (DGCNN) that accepts graphs of an arbitrary structure without the need to first convert them into vectors. The experimental workflow consists of two stages. In the first stage, a generative parametric system was designed to create a large synthetic dataset of an urban block with several typological categories. The geometric models were then automatically labelled and converted into semantically rich topological dual graphs. In the second stage, the dual graphs were imported into the DGCNN for graph classification. Experiments demonstrate that the proposed workflow achieves accuracy results that are highly competitive with DGCNN’s classification of benchmark graph datasets.

Item Type: Conference or Workshop Item (Paper)
Date Type: Published Online
Status: In Press
Schools: Architecture
Date of First Compliant Deposit: 20 May 2020
Date of Acceptance: 19 May 2020
Last Modified: 21 May 2020 14:45

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