Text2CAD: Text to 3D CAD Generation via Technical Drawings
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 10 of its 20 references that are in the map. First posted on arXiv 9 Nov 2024.
Builds on
10 of 20 references.
References · 10
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
- 2022DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generationcited by 3,286
- 2023Zero-1-to-3: Zero-shot One Image to 3D Objectcited by 1,483
- 2018ABC: A Big CAD Model Dataset For Geometric Deep Learningcited by 339
- 2023Unleashing Text-to-Image Diffusion Models for Visual Perceptioncited by 266
- 2022A Survey on Generative Diffusion Modelcited by 238
- 2023TextDiffuser: Diffusion Models as Text Painterscited by 223
- 2021CAPRI-Net: Learning Compact CAD Shapes with Adaptive Primitive Assemblycited by 62
- 2021CSG-Stump: A Learning Friendly CSG-Like Representation for Interpretable Shape Parsingcited by 51
- 2021Photo2CAD: Automated 3D solid reconstruction from 2D drawings using OpenCVcited by 4
Built on by
Citing papers per year: 2025: 5; 2026: 4.
9 papers.
- 2025Seek-CAD: A Self-refined Generative Modeling for 3D Parametric CAD Using Local Inference via DeepSeekcited by 22
- 2025CAD-Judge: Toward Efficient Morphological Grading and Verification for Text-to-CAD Generationcited by 9
- 2025Drawing2CAD: Sequence-to-Sequence Learning for CAD Generation from Vector Drawingscited by 9
- 2026Text-to-CAD Evaluation with CADTestscited by 9
- 2025Automated CAD Modeling Sequence Generation from Text Descriptions via Transformer-Based Large Language Modelscited by 8
- 2026MUSE: Benchmarking Manufacturable, Functional, and Assemblable Text-to-CAD Generationcited by 8
- 2025Empowering Real-World: A Survey on the Technology, Practice, and Evaluation of LLM-driven Industry Agentscited by 2
- 2026PrintAnything: Learning an Intermediate Representation for 3D printing G-code Generationcited by 0
- 2026RA-CAD: Learning Post-Execution Critique for State-Aware Text-to-CAD Generationcited by 0
- 2026RA-CAD: Learning Post-Execution Critique for State-Aware Text-to-CAD Generation6 Aug 2026
- 2026PrintAnything: Learning an Intermediate Representation for 3D printing G-code Generation30 Jul 2026
- 2026MUSE: Benchmarking Manufacturable, Functional, and Assemblable Text-to-CAD Generation27 May 2026
- 2026Text-to-CAD Evaluation with CADTests8 May 2026
- 2025Empowering Real-World: A Survey on the Technology, Practice, and Evaluation of LLM-driven Industry Agents20 Oct 2025
- 2025Drawing2CAD: Sequence-to-Sequence Learning for CAD Generation from Vector Drawings26 Aug 2025
- 2025CAD-Judge: Toward Efficient Morphological Grading and Verification for Text-to-CAD Generation6 Aug 2025
- 2025Automated CAD Modeling Sequence Generation from Text Descriptions via Transformer-Based Large Language Models26 May 2025
- 2025Seek-CAD: A Self-refined Generative Modeling for 3D Parametric CAD Using Local Inference via DeepSeek23 May 2025
Similar, not cited
Close in wording, no citation either way
- 2025CADKnitter: Compositional CAD Generation from Text and Geometry Guidance
- 2025Text-to-CadQuery: A New Paradigm for CAD Generation with Scalable Large Model Capabilities
- 2024GenCAD: Image-Conditioned Computer-Aided Design Generation with Transformer-Based Contrastive Representation and Diffusion Priors
- 2025GenCAD-3D: CAD Program Generation using Multimodal Latent Space Alignment and Synthetic Dataset Balancing
- 2026Img2CADSeq: Image-to-CAD Generation via Sequence-Based Diffusion
- 2025CAD-Coder: An Open-Source Vision-Language Model for Computer-Aided Design Code Generation
- 2024From 2D CAD Drawings to 3D Parametric Models: A Vision-Language Approach
- 2024CAD-Prompted Generative Models: A Pathway to Feasible and Novel Engineering Designs