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

TL;DR

This paper introduces a skip-connected neural network with layout graphs for automated floor plan generation, achieving high accuracy and demonstrating the effectiveness of multi-scale feature capture and graph-based decoding.

Contribution

It presents a novel neural network architecture combining skip connections and layout graphs for improved floor plan auto-generation.

Findings

Achieved 93.9 mIoU score on MSD dataset

Effective multi-scale information capture through skip connections

Validated on CVAAD workshop challenge

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.