Skip-Connected Neural Networks with Layout Graphs for Floor Plan Auto-Generation
This work addresses the problem of efficient floor plan design for architects and planners, representing an incremental improvement in domain-specific methods.
The paper tackled automated floor plan generation by integrating skip-connected neural networks with layout graphs, achieving a 93.9 mIoU score on the MSD dataset in a workshop challenge.
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.