In interior space planning, the furnishing stage usually entails manual iterative processes, including meeting design objectives, incorporating professional input, and optimizing design performance. Machine learning has the potential to automate and improve interior design processes, while maintaining creativity and quality. The aim of this study was to develop (1) A furnishing method that leverages machine learning, as a means for enhancing design processes, (2) Developing a set of evaluation metrics for assessing the quality of the generated results. To achieve these aims, we created a comprehensive dataset for training and evaluating three Conditional Generative Adversarial Network models (CGANs); pix2pix, BicycleGAN, and SPADE, to generate furniture layouts within given room boundaries. Finally, evaluation criteria that combine measures of architectural design with standard computer-vision parameters were devised. The visual architectural analyses and numerical results indicate that BicycleGAN outperformed the two other models while adhering to accepted architectural standards. The overall results demonstrate a machine-learning workflow that can be used to augment existing interior design processes.
Contact information: Hanan Tanasra | 050-6333703 | email@example.com
Thursday 20.07.2023 – at 11:00 – Amado 3rd Floor & via zoom meeting – click here