Floorplan Generation by Alternating Geometry and Semantics Optimization

Abstract Creating floorplans lays the foundation for architectural design and scene modeling. We propose a novel framework for generating diverse high‐quality floorplans under predefined constraints. Central to our method is an iterative refinement process for optimizing the bounding boxes of rooms and the floorplan semantics image, which defines a vector floorplan together. Vector floorplans can be generated through a learning‐based refinement process. Our framework supports various constraints, such as floorplan boundaries, topological graphs, and bubble diagrams. Extensive experiments demonstrate that our method is superior to state‐of‐the‐art techniques, particularly in generating a wider variety of solutions that cater to various architectural needs.