Deep Learning for Automated 3D Floor Plan Generation

Abstract: This paper presents an optimized method for reconstructing 3D floor plans using userdefined boundaries and constraints for residential structures. This approach allows users to providearchitectural constraints such as room types and quantities, as well as manual sketches or existingimages of the house boundaries. Advanced deep learning algorithms are used to automaticallypartition the house boundaries and create a customized, optimized interior layout based on the users’architectural constraints. In the experiment phase, we integrated the Graph2Plan-based deep learningmodule, which converts the user-provided boundary data and architectural constraints into astructured 2D floor plan, automatically allocating and refining the rooms to ensure a harmoniousspatial arrangement. The evaluation of the deep learning model’s performance shows that this is auseful and time-saving solution for designers. Then, we utilized graphics and image processingtechniques to generate the 3D floor plans. Based on this solution, we have developed a 3D floor plangeneration application that provides a flexible and adaptive solution for individual home planningwithin defined boundaries. The application has been thoroughly tested to demonstrate its features,including the ability to meet users’ architectural constraints, provide rapid response times, and offera convenient user interaction experience.Keywords: 3DFloorplan, Floorplan generation, layout graph, RPLAN dataset, house plan.