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Plan Generation from Program Graph
In this chapter, the authors explore a novel way of auto‐generation of architectural plans from bubble diagrams, a technique traditionally used by architects to arrange programs in two‐dimensional plan at the concept design stage. The systematic use of bubble diagrams can be dated back to as early as 1938. The recent development of the graph neural network gives us an opportunity to train models on graph‐structured data sets and generate semantically realistic floor plans, boosting the efficiency of generating massive plan variations in a much shorter time for the design industry. The authors propose a combined network consisting of three parts: graph embedding network, message passing network, and box&mask regression network. The message passing network is the core of the entire network setup.
Plan Generation from Program Graph
In this chapter, the authors explore a novel way of auto‐generation of architectural plans from bubble diagrams, a technique traditionally used by architects to arrange programs in two‐dimensional plan at the concept design stage. The systematic use of bubble diagrams can be dated back to as early as 1938. The recent development of the graph neural network gives us an opportunity to train models on graph‐structured data sets and generate semantically realistic floor plans, boosting the efficiency of generating massive plan variations in a much shorter time for the design industry. The authors propose a combined network consisting of three parts: graph embedding network, message passing network, and box&mask regression network. The message passing network is the core of the entire network setup.
Plan Generation from Program Graph
Carta, Silvio (editor) / Li,, Ao (author) / Tian,, Runjia (author) / Wang, and, Xiaoshi (author) / Lu, Yueheng (author)
Machine Learning and the City ; 167-170
2022-05-21
4 pages
Article/Chapter (Book)
Electronic Resource
English
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