In this paper, we propose Img2CAD, the first approach to our knowledge that uses 2D image inputs to generate CAD models with editable parameters. Unlike existing AI methods for 3D model generation using text or image inputs often rely on mesh-based representations, which are incompatible with CAD tools and lack editability and fine control, Img2CAD enables seamless integration between AI-based 3D reconstruction and CAD software. We have identified an innovative intermediate representation called Structured Visual Geometry (SVG), characterized by vectorized wireframes extracted from objects. This representation significantly enhances the performance of generating conditioned CAD models. Additionally, we introduce two new datasets to further support research in this area: ABC-mono, the largest known dataset comprising over 200,000 3D CAD models with rendered images, and KOCAD, the first dataset featuring real-world captured objects alongside their ground truth CAD models, supporting further research in conditioned CAD model generation.

翻译：本文提出Img2CAD方法，据我们所知，这是首个利用二维图像输入生成具有可编辑参数CAD模型的技术。与现有基于文本或图像输入、通常依赖网格表示的三维模型生成AI方法不同——这类网格表示与CAD工具不兼容且缺乏可编辑性与精细控制——Img2CAD实现了基于AI的三维重建与CAD软件间的无缝集成。我们提出了一种创新的中间表示方法，称为结构化视觉几何（SVG），其特征是从物体中提取的矢量化线框。该表示方法显著提升了生成条件化CAD模型的性能。此外，我们引入两个新数据集以进一步支持该领域研究：ABC-mono是目前已知最大的数据集，包含超过20万个附带渲染图像的三维CAD模型；KOCAD则是首个包含真实世界采集物体及其对应真实CAD模型的数据集，为条件化CAD模型生成的深入研究提供支持。