从单张图像生成可直接使用的工业级CAD模型。
Img2CADSeq: Image-to-CAD Generation via Sequence-Based Diffusion
- 用分层代码本编码CAD操作序列，压缩长序列到稳定离散空间。
- 通过点云中间表示对齐2D与3D特征，生成符合标准的STEP文件。
- 在CAD-220K和PrintCAD数据集上表现领先，适合工业设计场景。
边界表示（BRep）是计算机辅助设计（CAD）的标准格式，但仅凭单视角图像重建高质量的BReps仍具挑战性，主要源于拓扑约束与操作序列的复杂性。本文提出Img2CADSeq，一种多阶段流水线，通过将CAD序列编码为三级分层代码本，实现高效建模。该方法基于重要性优先策略，强调轮廓而非细节，将长序列压缩至稳定的离散潜在空间。为弥合模态差异，采用从粗到精的点云中间表示，利用对比学习对齐2D视觉特征与3D CAD序列，从而指导VQ-Diffusion模型生成。依托新构建的CAD-220K与PrintCAD数据集，模型具备强工业领域适应能力。大量实验表明，Img2CADSeq显著优于现有方法，生成的STEP文件可直接用于商业CAD软件。
原文摘要 · Abstract (English)
Boundary Representation (BRep) is the standard format for Computer-Aided Design (CAD), yet reconstructing high-quality BReps from single-view images remains challenging due to the complexity of topological constraints and operation sequences. We present Img2CADSeq, a multi-stage pipeline that overcomes these limitations by encoding CAD sequences into a three-level hierarchical codebook. Guided by an importance prioritization, this strategy values profiles over details, compressing long sequences into a stable discrete latent space. To bridge the modality gap, we leverage a coarse-to-fine point cloud intermediate, aligning 2D visual features with 3D CAD sequences via contrastive learning to condition a VQ-Diffusion model. Supported by newly introduced CAD-220K and PrintCAD datasets, our approach ensures robust industrial domain adaptation. Extensive experiments demonstrate that Img2CADSeq significantly outperforms state-of-the-art methods, producing standard STEP files that can be directly used in commercial CAD software.
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