用文字描述自动生成复杂CAD模型，提升设计效率。
Automated CAD Modeling Sequence Generation from Text Descriptions via Transformer-Based Large Language Models
- 用大模型自动标注参数与外观描述，构建高质量数据集
- 基于Transformer的双通道特征聚合生成建模序列，准确率更高
- 结合置信度反馈优化序列，适合工业设计自动化场景
复杂CAD模型设计常因计算效率低和建模精度难而耗时。本文提出一种语言引导的工业设计自动化框架，融合大语言模型（LLMs）与计算机辅助设计（CAutoD），实现从参数与外观描述自动生成CAD模型，支持详细设计阶段的任务自动化。提出三项创新：(1) 利用LLMs与视觉-语言大模型（VLLMs）的半自动数据标注流程，生成高质量参数与外观描述；(2) 提出基于Transformer的CAD生成器（TCADGen），通过双通道特征聚合预测建模序列；(3) 构建增强型生成模型CADLLM，结合TCADGen的置信度分数对生成序列进行优化。实验表明，该方法在准确率与效率上均优于传统方法，为工业工作流自动化和文本驱动复杂建模提供了有力工具。代码已公开于https://jianxliao.github.io/cadllm-page/
原文摘要 · Abstract (English)
Designing complex computer-aided design (CAD) models is often time-consuming due to challenges such as computational inefficiency and the difficulty of generating precise models. We propose a novel language-guided framework for industrial design automation to address these issues, integrating large language models (LLMs) with computer-automated design (CAutoD).Through this framework, CAD models are automatically generated from parameters and appearance descriptions, supporting the automation of design tasks during the detailed CAD design phase. Our approach introduces three key innovations: (1) a semi-automated data annotation pipeline that leverages LLMs and vision-language large models (VLLMs) to generate high-quality parameters and appearance descriptions; (2) a Transformer-based CAD generator (TCADGen) that predicts modeling sequences via dual-channel feature aggregation; (3) an enhanced CAD modeling generation model, called CADLLM, that is designed to refine the generated sequences by incorporating the confidence scores from TCADGen. Experimental results demonstrate that the proposed approach outperforms traditional methods in both accuracy and efficiency, providing a powerful tool for automating industrial workflows and generating complex CAD models from textual prompts. The code is available at https://jianxliao.github.io/cadllm-page/
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