Drawing2CAD：基於序列到序列學習的向量圖轉CAD生成

Drawing2CAD: Sequence-to-Sequence Learning for CAD Generation from Vector Drawings

August 26, 2025

作者: Feiwei Qin, Shichao Lu, Junhao Hou, Changmiao Wang, Meie Fang, Ligang Liu

cs.AI

摘要

電腦輔助設計（CAD）生成建模正在推動工業應用領域的重大創新。近期研究在從點雲、網格和文字描述等多種輸入創建實體模型方面取得了顯著進展。然而，這些方法與傳統工業工作流程存在根本性差異，後者通常始於二維工程圖紙。儘管從這些二維向量圖紙自動生成參數化CAD模型是工程設計中的關鍵步驟，但這一領域仍未被充分探索。為填補這一空白，我們的核心見解是將CAD生成重新定義為一個序列到序列的學習問題，其中向量圖元直接指導參數化CAD操作的生成，在整個轉換過程中保持幾何精度和設計意圖。我們提出了Drawing2CAD框架，該框架包含三個關鍵技術組件：一種保留精確幾何信息的網絡友好型向量圖元表示法，一種解耦命令類型和參數生成同時保持精確對應的雙解碼器變壓器架構，以及一種適應CAD參數內在靈活性的軟目標分佈損失函數。為訓練和評估Drawing2CAD，我們創建了CAD-VGDrawing數據集，其中包含配對的工程圖紙和參數化CAD模型，並通過全面實驗證明了我們方法的有效性。代碼和數據集可在https://github.com/lllssc/Drawing2CAD獲取。

English

Computer-Aided Design (CAD) generative modeling is driving significant innovations across industrial applications. Recent works have shown remarkable progress in creating solid models from various inputs such as point clouds, meshes, and text descriptions. However, these methods fundamentally diverge from traditional industrial workflows that begin with 2D engineering drawings. The automatic generation of parametric CAD models from these 2D vector drawings remains underexplored despite being a critical step in engineering design. To address this gap, our key insight is to reframe CAD generation as a sequence-to-sequence learning problem where vector drawing primitives directly inform the generation of parametric CAD operations, preserving geometric precision and design intent throughout the transformation process. We propose Drawing2CAD, a framework with three key technical components: a network-friendly vector primitive representation that preserves precise geometric information, a dual-decoder transformer architecture that decouples command type and parameter generation while maintaining precise correspondence, and a soft target distribution loss function accommodating inherent flexibility in CAD parameters. To train and evaluate Drawing2CAD, we create CAD-VGDrawing, a dataset of paired engineering drawings and parametric CAD models, and conduct thorough experiments to demonstrate the effectiveness of our method. Code and dataset are available at https://github.com/lllssc/Drawing2CAD.