增强检索的草图引导3D建筑生成
Retrieval-Augmented Sketch-Guided 3D Building Generation
AI总结：
本文提出多阶段3D生成框架，通过结合生成与检索方法实现组件级编辑与个性化定制，解决日本独栋房屋早期设计阶段因缺乏统一表示导致的设计漂移问题。
摘要：
在日式独栋房屋早期设计阶段，客户、销售代表和设计师之间缺乏统一的设计表示导致设计漂移和反馈低效。通常，销售代表传递的草图在快速绘制时会丢失细节，从而降低后续使用生成式AI模型生成3D模型的保真度。生成的3D模型通常以单一统一网格形式存在，防止组件级编辑。为了解决这些问题，我们提出了一种多阶段3D生成设计框架，能够从粗糙的设计草图生成建筑模型。该框架结合了生成和基于检索的方法，以实现组件级编辑和个性化定制。它采用多模态表示进行3D模型生成，并应用组件分割来定位如窗户和门等建筑组件，并利用检索支持对组件的定向替换。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。本工作引入了针对日式独栋房屋的多阶段草图到3D框架，提供了立面和组件数据集，并通过定量和专家评估展示了有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
