Obj2CAD: Object-Oriented Text-to-CAD Generation with Large Language Models

Shifu Hou, Lingwei Chen, Rick Johnson, Ramandeep Singh Makhija, Yanfang Ye, Nitesh V Chawla

Keywords: CAD, LLM, Graph, Object-Oriented

Abstract: Text-to-CAD aims to generate CAD models directly from natural language descriptions. Existing methods predominantly follow a procedural paradigm that represents modeling as long sequences of operations. This approach suffers from several inherent limitations: strictly order-dependent, prone to error accumulation, cluttered with redundant low-level constraints, and misaligned with the object-centric reasoning of both human designers and large language models (LLMs). We introduce Obj2CAD, the first framework to shift text-to-CAD from a procedural to an object-oriented paradigm. To support this shift, we curate a new dataset of 1,000 examples, including both industrial parts and carefully selected shapes that are linguistically describable. Each example is converted into an object-oriented representation that emphasizes hierarchical structure and semantic constraints while de-emphasizing redundant low-level constraints. Building on this foundation, we design an LLM-driven framework that combines top-down planning with bottom-up generation, offering a divide-and-conquer approach to text-to-CAD. To enhance spatial reliability, we propose geometric assembly reasoning to formulate assembly explicitly as geometric-mathematical problems. Finally, we introduce an interactive iterative mechanism that incorporates user feedback to refine objects and expand the object graph, enabling continuous system improvement. We provide a live demo of Obj2CAD and will publicly release the dataset to support future research on object-oriented CAD generation.

Primary Area: generative models

Submission Number: 24295