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# Img2CADSeq

**[SIGGRAPH 2026] Img2CADSeq: Image-to-CAD Generation via Sequence-Based Diffusion**

[![Project Page](https://img.shields.io/badge/Project-Page-blue)](https://rilpraa0110.github.io/Img2CADSeq/)
[![arXiv](https://img.shields.io/badge/arXiv-2605.13293-b31b1b.svg)](https://arxiv.org/abs/2605.13293)
[![GitHub](https://img.shields.io/badge/GitHub-Code-black)](https://github.com/Rilpraa0110/Img2CADSeq)

Official repository for **Img2CADSeq**, a pipeline that outputs standardized STEP files.

---


## ๐Ÿ“Š Datasets

To train and evaluate our framework, we introduce two distinct data types: curated synthetic models and real-world captured objects.

### CAD-220K (Synthetic)
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:
* **40K** models (1โ€“10 faces)
* **120K** models (11โ€“50 faces)
* **30K** models (51โ€“100 faces)
* **30K** models (>100 faces)

> 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`.

### PrintCAD (Real-world)
To explore sim-to-real translation, we introduce PrintCAD, a collection of 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. 
> ๐Ÿ“ **Repository Files:** The complete PrintCAD dataset is available for download in this repository as `PrintCAD.zip`.

---

## ๐Ÿ“ Citation

If you find our work or datasets useful in your research, please consider citing:

```bibtex
@article{tan2026img2cadseq,
  title={Img2CADSeq: Image-to-CAD Generation via Sequence-Based Diffusion},
  author={Tan, Shiyu and Zhao, Zixuan and Gao, Hao and Chen, Zhiheng and Yin, Xiaolong and Shen, Enya},
  journal={arXiv preprint arXiv:2605.13293},
  year={2026}
}