Limitations of cGAN in functional area division for interior design

Sammanfattning: A process that historically has been hard to automate is interior design, mainly due to its subjective nature and lack of obvious guidelines. Scientifically, there is interest to examine if subjective processes can be automated using black box algorithms such as neural networks, as well as corporate interest in this subject to increase efficiency and create systems for automated floor plan design. This work focuses on finding the limitations of such a project, mainly in establishing the threshold of data points needed for an algorithm in this area to generate relevant results as well as an investigation into requirements to make systems of this kind incorporated in a production pipeline. In this work floor plans with functional area division were set out to be generated using a conditional, generative, adversarial network, cGAN. The system is applied on a use-case provided by NORNORM, a company providing a circular, subscription-based furnishing service for office spaces, also providing data in the form of floor plans. The algorithm is inspired by Yang et al.’s stateof-the-art model from 2019 and the network is tested with three data sets of different sizes, consisting of 100, 500 and 1000 floor plans respectively. This work includes a quantitative evaluation inspired by by Di and Yu, using the average intersection over union metric. Additionally, this work proposes a qualitative evaluation. The qualitative evaluation is carried out using interior designers, posed with a subjective, two-alternative, forced-choice (2AFC) approval or disapproval of the design as a first draft to a customer. This evaluation was not conducted due to insufficient results. The generated results suggests that the threshold for data lies above 1000 data points and, compared to the work by Yang et al., below 4000 data points, the quantitative evaluation concurred with this statement. This interval could be narrowed in future work. In relation as to whether or not the system could be production ready there are a few requirements unachieved, for instance automated data collection and preprocessing. Future work could include conducting the qualitative evaluation on a future implementation of this system.