Enhancing Architectural Floor Plans with CycleGAN

The client wanted to revolutionize architectural visualization by developing a generative adversarial network (GAN) solution to transform hand-drawn or black-and-white floor plans into visually appealing, modern designs. The primary objective was to enhance the presentation quality of floor plans and provide a seamless user experience.

The client envisioned a user-friendly platform accessible to architects, designers, and clients alike. The platform would enable them to effortlessly upload floor plans and witness their transformation into captivating visual representations.

Additionally, the platform would integrate with the existing architectural workflows for seamless adoption. By aligning with industry standards and common design tools, the solution would facilitate a smooth transition from traditional to modernized floor plans, enhancing workflow efficiency.

We developed an intuitive platform leveraging CycleGAN, a deep-learning architecture that facilitates unsupervised image translation. It helped us develop a solution that meets the client's requirements—the seamless transformation of floor plans into modern, appealing designs. After careful consideration and evaluation, we chose CycleGAN for its capabilities to produce remarkable results without the need for a paired dataset. An added bonus is its powerful unpaired training capabilities and ability to work well with texture and color changes.

• Enhanced Visual Appeal
• Cost Efficiency
• Improved Performance
• Reduced Time and Resources
• Consistency in Design

• Unpaired Image Translation: CycleGAN performs image translation without requiring paired examples of corresponding images. This flexibility is crucial for tasks where obtaining paired datasets is challenging or impractical, such as transforming floor plans where corresponding colorful versions may not exist.
• Cycle-Consistency Loss: CycleGAN incorporates cycle-consistency loss, which ensures that the reconstructed image from the translated image is close to the original input. This helps maintain the identity of the input image during translation, leading to more realistic and coherent outputs.
• Scalability and Performance: CycleGAN is capable of handling large datasets efficiently and can be trained on modern GPU hardware, making it scalable for diverse applications, including architectural visualization.
• Bidirectional Image Translation: CycleGAN supports bidirectional image translation, meaning it can learn to transform images from domain A to domain B as well as from domain B to domain A simultaneously. This bidirectional mapping ensures that the model can handle transformations in both directions.
• Open-Source Implementation: CycleGAN is available as an open-source implementation, making it accessible to researchers and developers for experimentation, customization, and integration into various projects.

• CycleGAN
• PyTorch