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arXiv · 2605.13293

Img2CADSeq: Image-to-CAD Generation via Sequence-Based Diffusion

Shiyu Tan·Zixuan Zhao·Hao Gao·Zhiheng Chen·Xiaolong Yin·Enya Shen
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Abstract

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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Shiyu Tan, Zixuan Zhao, Hao Gao, Zhiheng Chen, Xiaolong Yin, Enya Shen. 2026-05-13. Img2CADSeq: Image-to-CAD Generation via Sequence-Based Diffusion. https://doi.org/10.1145/3799902.3811174

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