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.
Abstract (translated)
在建筑设计过程中，平面图设计通常是一个动态且迭代的过程。建筑师根据自己的创意和需求逐步绘制平面图的不同部分，并在整个设计过程中不断调整和完善。因此，从不完整的平面图预测出完整的设计具有重要的价值，这可以帮助建筑师快速生成初步设计方案，提高设计效率并减少反复修改的工作量。为了解决这一问题，我们提出了FloorplanMAE，这是一个基于自监督学习框架的系统，用于将不完整的平面图恢复成完整的平面图。 首先，我们开发了一个专门针对建筑平面图进行训练的数据集——FloorplanNet。其次，我们提出了一种基于掩码自动编码器（Masked Autoencoders, MAE）的方法来重建缺失部分的平面图设计：通过遮蔽平面图中的某些区域，并使用轻量级的视觉变换器（Vision Transformer, ViT）进行训练。 我们在多个标准基准上评估了FloorplanMAE的重建准确性，并与现有最先进的方法进行了比较。此外，我们还利用建筑设计初期的真实草图验证了模型的有效性。实验结果表明，FloorplanMAE能够从不完整的平面设计中生成高质量的完整平面图。这一框架为平面图的设计提供了一个可扩展的解决方案，在实际应用中具有广阔的应用前景。
URL
https://arxiv.org/abs/2506.08363
