Img2CAD: Conditioned 3-D CAD Model Generation From Single Image With Structured Visual Geometry

In this article, we propose Img2CAD, the first approach to our knowledge that uses 2-D image inputs to generate computer-aided design (CAD) models with editable parameters. Unlike existing artificial intelligence (AI) methods for 3-D 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 3-D reconstruction and CAD software. We have identified an innovative intermediate representation called structured visual geometry, characterized by vectorized wireframes extracted from objects. This representation significantly enhances the performance of generating conditioned CAD models. In addition, we introduce two new datasets to further support research in this area: a big cad model dataset (ABC)-mono, the largest known dataset comprising over 200 000 3-D 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.