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Found 5 papers, 3 papers with code
House-GAN++: Generative Adversarial Layout Refinement Network towards Intelligent Computational Agent for Professional Architects
This paper proposes a generative adversarial layout refinement network for automated floorplan generation.
Building-GAN: Graph-Conditioned Architectural Volumetric Design Generation
Volumetric design is the first and critical step for professional building design, where architects not only depict the rough 3D geometry of the building but also specify the programs to form a 2D layout on each floor.
House-GAN++: Generative Adversarial Layout Refinement Networks
This paper proposes a novel generative adversarial layout refinement network for automated floorplan generation.
Learning to simulate and design for structural engineering
However, structural engineers in practice often avoid optimization and compromise on a suboptimal design for the majority of buildings, due to the large size of the design space, the iterative nature of the optimization methods, and the slow simulation tools.
House-GAN: Relational Generative Adversarial Networks for Graph-constrained House Layout Generation
This paper proposes a novel graph-constrained generative adversarial network, whose generator and discriminator are built upon relational architecture.
