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Research of Modeling Skyline of Hongkong Central with OpenSCAD and Blender
- 1 Faculty of engineering, University of Sydney, Camperdown NSW 2050, Australia
* Author to whom correspondence should be addressed.
Abstract
Hong Kong, as an international financial center, is widely admired for its iconic Central skyline. Central is not only the heart of the economy but also a hub for global financial activities, gathering numerous international banks and financial institutions. This study aims to explore the feasibility and efficiency of using OpenSCAD for 3D modeling of some buildings in the Central district of Hong Kong, thereby providing a new tool for architectural design and urban planning.In this study, this paper selected three representative buildings for 3D modeling: the Bank of China Tower, the International Finance Centre II, and the Hong Kong Ferris Wheel. These buildings are not only distinctive in their own right but also occupy significant positions in the Central skyline. Through their modeling, we demonstrated how to generate complex geometric structures using a programmatic approach, fully reflecting the efficiency and flexibility of OpenSCAD as a modeling tool. The results show that OpenSCAD has a clear advantage in handling regular geometric bodies and repetitive structures, especially in parametric and modular model design. In addition, by using OpenSCAD's scripted modeling capabilities, we can easily create buildings with different design intentions and styles, further enriching the three-dimensional visual effects of the city.
Keywords
OpenSCAD, Hongkong central, building model.
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Cite this article
Zhang,Z. (2024). Research of Modeling Skyline of Hongkong Central with OpenSCAD and Blender. Applied and Computational Engineering,110,55-64.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of CONF-MLA 2024 Workshop: Securing the Future: Empowering Cyber Defense with Machine Learning and Deep Learning
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