In the early design stage of Japanese detached houses, the lack of a unified design representation among clients, sales representatives, and designers leads to design drift and inefficient feedback. Usually, sketches handed off by sales representatives may lose details for quick drawing, which reduces the fidelity of subsequent 3D generation using generative AI models. The generated 3D model typically takes the form of a single unified mesh, preventing component-level editing. To solve these issues, we propose a multi-stage 3D generative design framework capable of producing architectural models from rough design sketches. The framework combines generative and retrieval-based methods to enable component-level editing and personalized customization. It adopts a multimodal representation for 3D model generation and applies component segmentation to localize architectural components such as windows and doors and uses retrieval to support targeted replacement of components. Experiments show that the work enables modular customization which is thought to be suitable for personalized architectural design. This work introduces a multi-stage sketch-to-3D framework for Japanese detached houses, provides facade and component datasets, and shows effectiveness through quantitative and expert evaluations.

翻译：在日本独立住宅的早期设计阶段，由于客户、销售代表和设计师之间缺乏统一的设计表达方式，常导致设计偏离和反馈效率低下。通常，销售代表移交的草图可能因快速绘制而丢失细节，从而降低了使用生成式AI模型进行后续三维生成的真实性。生成的三维模型通常采用单一整体网格的形式，阻碍了组件级别的编辑。为解决这些问题，我们提出了一种能够从粗略设计草图生成建筑模型的多阶段三维生成设计框架。该框架结合了生成式和检索式方法，以实现组件级别的编辑和个性化定制。它采用多模态表示进行三维模型生成，并应用组件分割来定位窗户、门等建筑构件，同时利用检索技术支持针对性的组件替换。实验表明，该工作实现了模块化定制，被认为适用于个性化建筑设计。本研究为日本独立住宅引入了一个多阶段的草图到三维生成框架，提供了立面及组件数据集，并通过定量评估与专家评估验证了其有效性。