Abstract
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.
J. Liu and Y. Xue—These authors contributed equally to this work.
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Acknowledgements
This work is supported in part by NSF Award #1815491. We appreciate the help from professors and graduate students from College of Arts and Architecture at Penn State with the user study. We also would like to thank Enyan Dai for meaningful discussions on GNN.
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Liu, J., Xue, Y., Duarte, J., Shekhawat, K., Zhou, Z., Huang, X. (2022). End-to-End Graph-Constrained Vectorized Floorplan Generation with Panoptic Refinement. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13675. Springer, Cham. https://doi.org/10.1007/978-3-031-19784-0_32
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