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Title: Generative design of decorative architectural parts
Authors: Zhang, Yuzhe
Ong, Chan Chi
Zheng, Jianmin
Lie, Seng Tjhen
Guo, Zhendong
Keywords: Engineering::Civil engineering
Issue Date: 2022
Source: Zhang, Y., Ong, C. C., Zheng, J., Lie, S. T. & Guo, Z. (2022). Generative design of decorative architectural parts. Visual Computer, 38(4), 1209-1225.
Project: 2017-T2-1-076
Journal: Visual Computer
Abstract: This paper presents a method for generative design of decorative architectural parts such as corbel, moulding and panel, which usually have clear structure and aesthetic details. The method is composed of two components: offline learning and online generation. The offline learning trains a 2D CurveInfoGAN and a 3D VoxelVAE that learn the feature representations of the parts in a dataset. The online generation proceeds with an evolution procedure that evolves to product new generation of part components by selecting, crossing over and mutating features, followed by a feature-driven deformation that synthesizes the 3D mesh representation of new models. Built upon these technical components, a generative design tool is developed, which allows the user to input a decorative architectural model as a reference and then generates a set of new models that are “more of the same” as the reference and meanwhile exhibit some “surprising” elements. The experiments demonstrate the effectiveness of the method and also showcase the use of classic geometric modelling and advanced machine learning techniques in modelling of architectural parts.
ISSN: 0178-2789
DOI: 10.1007/s00371-021-02142-1
Schools: School of Civil and Environmental Engineering 
School of Computer Science and Engineering 
Rights: © 2021 The Authors, under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
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