With the advent of AI and computer vision techniques, the quest for automated and efficient floor plan designs has gained momentum. This paper presents a novel approach using skip-connected neural networks integrated with layout graphs. The skip-connected layers capture multi-scale floor plan information, and the encoder-decoder networks with GNN facilitate pixel-level probability-based generation. Validated on the MSD dataset, our approach achieved a 93.9 mIoU score in the 1st CVAAD workshop challenge. Code and pre-trained models are publicly available at https://github.com/yuntaeJ/SkipNet-FloorPlanGe.
@article{arxiv.2309.13881,
title = {Skip-Connected Neural Networks with Layout Graphs for Floor Plan Auto-Generation},
author = {Yuntae Jeon and Dai Quoc Tran and Seunghee Park},
journal= {arXiv preprint arXiv:2309.13881},
year = {2023}
}