Skip-Connected Neural Networks with Layout Graphs for Floor Plan Auto-Generation

• URL: http://arxiv.org/abs/2309.13881v2
• Date: Tue, 26 Sep 2023 03:16:31 GMT
• Title: Skip-Connected Neural Networks with Layout Graphs for Floor Plan Auto-Generation
• Authors: Yuntae Jeon, Dai Quoc Tran, Seunghee Park
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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