用代码思维理解三维模型，让大模型统一处理多种建模任务。
BrepCoder: A Unified Multimodal Large Language Model for Multi-task B-rep Reasoning
- 将B-rep模型转为类代码，用大模型解析设计逻辑。
- 两阶段训练：先逆向工程学几何特征，再拓展到补全、纠错等任务。
- 无需改结构即可应对多种建模需求，适合工业设计与自动化场景。
深度学习在计算机辅助设计（CAD）领域取得进展，但现有方法多依赖特定任务模型，需结构调整，且聚焦点云或图像，而非行业标准的边界表示（B-rep）格式。为此，我们提出BrepCoder，一种统一的多模态大语言模型（MLLM），可从B-rep输入执行多样化的CAD任务。通过利用大语言模型（LLM）的代码生成能力，我们将建模序列转换为类Python代码，并与B-rep对齐。采用两阶段训练策略：首先在逆向工程任务上预训练，学习几何特征与设计逻辑；其次有效扩展至下游任务，如模型补全、错误纠正和CAD问答。由此，通过将B-rep视为结构化代码，BrepCoder在多样化任务中实现优异泛化能力，展现出作为通用CAD代理的潜力。
原文摘要 · Abstract (English)
Recent advancements in deep learning have actively addressed complex challenges within the Computer-Aided Design (CAD) domain.However, most existing approaches rely on task-specifi c models requiring structural modifi cations for new tasks, and they predominantly focus on point clouds or images rather than the industry-standard Boundary Representation (B-rep) format. To address these limitations, we propose BrepCoder, a unifi ed Multimodal Large Language Model (MLLM) that performs diverse CAD tasks from B-rep inputs. By leveraging the code generation capabilities of Large Language Models (LLMs), we convert CAD modeling sequences into Python-like code and align them with B-rep. We then adopt a two-stage training strategy: First, pre-training on reverse engineering to learn geometric features and design logic. Second, eff ectively extending the model to various downstream tasks such as completion, error correction, and CAD-QA. Consequently, by interpreting B-rep as structural code, BrepCoder achieves superior generalization across diverse tasks, demonstrating its potential as a general-purpose CAD agent.
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