CAD-Tokenizer：基於模態特定分詞的文本驅動CAD原型設計

摘要

電腦輔助設計（CAD）是工業原型設計的基礎組成部分，其中模型並非由原始座標定義，而是通過草圖和擠出等建構序列來定義。這種序列結構既支持高效的原型初始化，也便於後續編輯。文本引導的CAD原型設計，將文本到CAD生成與CAD編輯統一起來，有潛力簡化整個設計流程。然而，先前的研究尚未探索這一設定，主要是因為標準的大型語言模型（LLM）分詞器將CAD序列分解為自然語言詞片段，未能捕捉到原始級別的CAD語義，阻礙了注意力模組對幾何結構的建模。我們推測，與CAD的原始和結構特性相契合的多模態分詞策略，能提供更有效的表示。為此，我們提出了CAD-Tokenizer，這是一個利用基於序列的VQ-VAE（帶有原始級別池化和約束解碼）來表示CAD數據的框架，該框架使用模態特定的標記。這一設計產生了緊湊、原始感知的表示，與CAD的結構特性相吻合。應用於統一的文本引導CAD原型設計時，CAD-Tokenizer顯著提升了指令遵循和生成質量，在定量和定性表現上均優於通用LLM及特定任務基線。

English

Computer-Aided Design (CAD) is a foundational component of industrial prototyping, where models are defined not by raw coordinates but by construction sequences such as sketches and extrusions. This sequential structure enables both efficient prototype initialization and subsequent editing. Text-guided CAD prototyping, which unifies Text-to-CAD generation and CAD editing, has the potential to streamline the entire design pipeline. However, prior work has not explored this setting, largely because standard large language model (LLM) tokenizers decompose CAD sequences into natural-language word pieces, failing to capture primitive-level CAD semantics and hindering attention modules from modeling geometric structure. We conjecture that a multimodal tokenization strategy, aligned with CAD's primitive and structural nature, can provide more effective representations. To this end, we propose CAD-Tokenizer, a framework that represents CAD data with modality-specific tokens using a sequence-based VQ-VAE with primitive-level pooling and constrained decoding. This design produces compact, primitive-aware representations that align with CAD's structural nature. Applied to unified text-guided CAD prototyping, CAD-Tokenizer significantly improves instruction following and generation quality, achieving better quantitative and qualitative performance over both general-purpose LLMs and task-specific baselines.

CAD-Tokenizer：基於模態特定分詞的文本驅動CAD原型設計

CAD-Tokenizer: Towards Text-based CAD Prototyping via Modality-Specific Tokenization

摘要

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