FloorplanSBS: Synthesizing Vector Floorplans by Patch-Based Floorplan Segmentation

Automated vector floorplan generation is valuable for designers to explore potential spatial designs. However, existing learning-based methods rely on complex post-processing or optimization to obtain plausible vector floorplans, which disrupts the end-to-end design flow. In this paper, we propose FloorplanSBS, a patch-based segmentation framework for directly synthesizing vector floorplans. Our method leverages the strengths of box-based representation and segmentation-based generation, following a division-and-labeling scheme. The framework operates in two stages: given input design constraints, a division model first divides the design space into rectangular patches, followed by a labelling model that assigns semantic labels to each patch. FloorplanSBS supports constraints such as boundaries and layout graphs. Extensive evaluations show that it surpasses state-of-the-art methods in generating high-quality vector floorplans. With its end-to-end neural framework, FloorplanSBS eliminates the need for post-processing, offering a simple, efficient, and user-friendly tool for vector floorplan design.