Novel House GAN

Registration ID: IJNRD_189892

Published ID: IJNRD2303434

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Prafulkumar Ponnappan, Rahul Rajesh, Rishit Kurup, Suyog Yadav

Floor Plans, Architecture, Generative Adversarial Network

This paper presents a novel approach for generating floor plans using machine learning techniques, specifically Generative Adversarial Networks (GANs). The implemented method utilizes a bubble graph, which is a rough layout generated by an architect, as input for the GANs. The GANs process the bubble graph and quickly iterate multiple floor plan solutions based on user-specified constraints such as the number of rooms, room sizes, and spatial layout. The generated plans are evaluated against a set of quality metrics such as plan feasibility and aesthetics. The results show that the proposed method is able to generate multiple high-quality floor plans that are comparable to those designed by human experts. Additionally, the approach can significantly reduce the time and cost associated with the traditional floor plan design process. The customer and architect can select a suitable layout that will be converted into a 2D floorplan by the GAN. The implications of this research are significant for the fields of architecture and building design, as it has the potential to revolutionize the way in which floor plans are generated in the future.

Published Paper   E-Certificate

Prafulkumar Ponnappan, Rahul Rajesh, Rishit Kurup, & Suyog Yadav (March-2023). Novel House GAN. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(3), e277-e282. https://ijnrd.org/papers/IJNRD2303434.pdf

Volume 8 Issue 3, March-2023

Pages : e277-e282

Paper Reg. ID: IJNRD_189892

Published Paper Id: IJNRD2303434

Downloads: 000121988

Research Area: Computer Engineering 

Author Type: Indian Author

Country: Airoli, Maharashtra, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2303434.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2303434

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