README.md

# 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.

---


## 📊 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 11–50 faces constitute the vast majority (339,489 in total), we proportionally downsample the data to establish balanced complexity tiers:
* **40K** models (1–10 faces)
* **120K** models (11–50 faces)
* **30K** models (51–100 faces)
* **30K** models (>100 faces)

> 📁 **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. 
> 📁 **Repository Files:** The complete PrintCAD dataset is available for download in this repository as `PrintCAD.zip`.

---

## 📝 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}
}