  The automatic generation of floorplans given user inputs has great potential
in architectural design and has recently been explored in the computer vision
community. However, the majority of existing methods synthesize floorplans in
the format of rasterized images, which are difficult to edit or customize. In
this paper, we aim to synthesize floorplans as sequences of 1-D vectors, which
eases user interaction and design customization. To generate high fidelity
vectorized floorplans, we propose a novel two-stage framework, including a
draft stage and a multi-round refining stage. In the first stage, we encode the
room connectivity graph input by users with a graph convolutional network
(GCN), then apply an autoregressive transformer network to generate an initial
floorplan sequence. To polish the initial design and generate more visually
appealing floorplans, we further propose a novel panoptic refinement
network(PRN) composed of a GCN and a transformer network. The PRN takes the
initial generated sequence as input and refines the floorplan design while
encouraging the correct room connectivity with our proposed geometric loss. We
have conducted extensive experiments on a real-world floorplan dataset, and the
results show that our method achieves state-of-the-art performance under
different settings and evaluation metrics.
