FloorplanMAE:A self-supervised framework for complete floorplan generation from partial inputs

TL;DR

FloorplanMAE is a self-supervised framework that effectively reconstructs complete architectural floorplans from partial inputs using masked autoencoders and Vision Transformers, aiding architects in early design stages.

Contribution

It introduces a novel self-supervised learning approach with a dedicated dataset and a masked autoencoder method for complete floorplan reconstruction from partial designs.

Findings

High reconstruction accuracy demonstrated on FloorplanNet dataset

Effective in real-world sketch-based floorplan completion

Outperforms existing benchmark methods

Abstract

In the architectural design process, floorplan design is often a dynamic and iterative process. Architects progressively draw various parts of the floorplan according to their ideas and requirements, continuously adjusting and refining throughout the design process. Therefore, the ability to predict a complete floorplan from a partial one holds significant value in the design process. Such prediction can help architects quickly generate preliminary designs, improve design efficiency, and reduce the workload associated with repeated modifications. To address this need, we propose FloorplanMAE, a self-supervised learning framework for restoring incomplete floor plans into complete ones. First, we developed a floor plan reconstruction dataset, FloorplanNet, specifically trained on architectural floor plans. Secondly, we propose a floor plan reconstruction method based on Masked Autoencoders (MAE), which reconstructs missing parts by masking sections of the floor plan and training a lightweight Vision Transformer (ViT). We evaluated the reconstruction accuracy of FloorplanMAE and compared it with state-of-the-art benchmarks. Additionally, we validated the model using real sketches from the early stages of architectural design. Experimental results show that the FloorplanMAE model can generate high-quality complete floor plans from incomplete partial plans. This framework provides a scalable solution for floor plan generation, with broad application prospects.