2024/11/09 by Mohsen Yavartanoo, Sangmin Hong, Yavartanoo, Mohsen +5 · 4 citations
Computer Science · Engineering · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Human Motion and Animation #Image Processing and 3D Reconstruction #Mathematics, Computing, and Information Processing
paper · pdf · doi:10.48550/arxiv.2411.06206
openalex publication_date 2024/11/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The generation of industrial Computer-Aided Design (CAD) models from user requests and specifications is crucial to enhancing efficiency in modern manufacturing. Traditional methods of CAD generation rely heavily on manual inputs and struggle with complex or non-standard designs, making them less suited for dynamic industrial needs. To overcome these challenges, we introduce Text2CAD, a novel framework that employs stable diffusion models tailored to automate the generation process and efficiently bridge the gap between user specifications in text and functional CAD models. This approach directly translates the user's textural descriptions into detailed isometric images, which are then precisely converted into orthographic views, e.g., top, front, and side, providing sufficient information to reconstruct 3D CAD models. This process not only streamlines the creation of CAD models from textual descriptions but also ensures that the resulting models uphold physical and dimensional consistency essential for practical engineering applications. Our experimental results show that Text2CAD effectively generates technical drawings that are accurately translated into high-quality 3D CAD models, showing substantial potential to revolutionize CAD automation in response to user demands.