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- # Img2CADSeq
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- **[SIGGRAPH 2026] Img2CADSeq: Image-to-CAD Generation via Sequence-Based Diffusion**
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- [![Project Page](https://img.shields.io/badge/Project-Page-blue)](https://rilpraa0110.github.io/Img2CADSeq/)
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- [![arXiv](https://img.shields.io/badge/arXiv-2605.13293-b31b1b.svg)](https://arxiv.org/abs/2605.13293)
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- [![GitHub](https://img.shields.io/badge/GitHub-Code-black)](https://github.com/Rilpraa0110/Img2CADSeq)
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- Official repository for **Img2CADSeq**, a state-of-the-art Boundary Representation (BRep) pipeline that outputs standardized STEP files directly from single-view images, point clouds, or unconditionally.
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- ---
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- ## ๐Ÿ“– Abstract
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- 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.
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- 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.
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- 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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- ---
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- ## ๐Ÿ“Š Datasets
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- To train and evaluate our framework, we introduce two distinct data types: curated synthetic models and real-world captured objects. Both datasets are exclusively utilized to fine-tune our image-to-point-cloud module, ensuring that the point cloud embeddings align with the CAD latent space.
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- ### CAD-220K (Synthetic)
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- To support the point cloud lifting stage, we utilize CAD-220K, a curated subset of the ABC dataset filtered by surface count. Observing that models with 11โ€“50 faces constitute the vast majority (339,489 in total), we proportionally downsample the data to establish balanced complexity tiers:
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- * **40K** models (1โ€“10 faces)
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- * **120K** models (11โ€“50 faces)
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- * **30K** models (51โ€“100 faces)
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- * **30K** models (>100 faces)
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- For these 220K models, we generate the corresponding STLs, point clouds, and four-view rendered images.
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- > ๐Ÿ“ **Repository Files:** The filtered surface count lists and selected IDs for the CAD-220K dataset are available in this repository as `all_surfs_result.csv` and `all_surfs_result.json`.
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- ### PrintCAD (Real-world)
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- To explore sim-to-real translation, we introduce PrintCAD, a collection of over 2,000 3D-printed solids. For each model, we systematically capture four views under real-world lighting. These objects exhibit manufacturing artifacts and texture noise, offering an evaluation of model robustness beyond synthetic renders.
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- > ๐Ÿ“ **Repository Files:** The complete PrintCAD dataset is available for download in this repository as `PrintCAD.zip`.
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- ---
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- ## โš™๏ธ Workflow Overview
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- At the base level, the **Curve-Cluster** parameterizes geometric primitives, which form closed loops in the **Sketch-Patch**. These loops are then lifted into 3D space via a normal vector and origin to perform extrusion and Boolean operations, resulting in an **Extrude-Block**. Multiple blocks are finally assembled to yield the target solid. This process mirrors the construction history of standard CAD workflows, preserving human design intent.
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- ---
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- ## ๐Ÿ“ Citation
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- If you find our work or datasets useful in your research, please consider citing:
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- ```bibtex
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- @article{tan2026img2cadseq,
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- title={Img2CADSeq: Image-to-CAD Generation via Sequence-Based Diffusion},
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- author={Tan, Shiyu and Zhao, Zixuan and Gao, Hao and Chen, Zhiheng and Yin, Xiaolong and Shen, Enya},
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- journal={arXiv preprint arXiv:2605.13293},
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- year={2026}
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- }