Bingyang Zhou, Haoyu Zhou, Tianhai Liang, Qiaojun Yu, Siheng Zhao, Yuwei Zeng, Jun Lv, Siyuan Luo, Qiancai Wang, Xinyuan Yu, Haonan Chen, Cewu Lu, and Lin Shao
We present ClothesNet: a large-scale dataset of 3D clothes objects with information-rich annotations. Our dataset consists of around 4400 models covering 11 categories annotated with clothes features, boundary lines, and key points. ClothesNet can be used to facilitate a variety of computer vision and robot interaction tasks. Using our dataset, we establish benchmark tasks for clothes perception, including classification, boundary line segmentation, and keypoint detection, and develop simulated clothes environments for robotic interaction tasks, including rearranging, folding, hanging, and dressing. We also demonstrate the efficacy of our ClothesNet in real-world experiments.
In each cloth folder, the following files are included：
Cloth mesh stored in obj format along with corresponding mtl file and texture images.
Cloth border stored in obj format along with corresponding mtl file.
Key points point cloud stored in pcd format (including ten points) and a point cloud of 2048 sampled points from the cloth mesh.
Please read carefully the following terms and conditions. By downloading and/or using the data, you acknowledge that you have read the following terms and conditions, understand them, and agree to be bound by them:
For non-commercial and scientific research purposes only.
You are not allowed to forward or distribute the data.
Get in touch with us by sending an email to byzhou828@gmail.com or linshao@nus.edu.sg if you have any question.
@inproceedings{zhou2023clothesnet,
title={ClothesNet: An Information-Rich 3D Garment Model Repository with Simulated Clothes Environment},
author={Bingyang Zhou and Haoyu Zhou and Tianhai Liang and Qiaojun Yu and Siheng Zhao and Yuwei Zeng and Jun Lv and Siyuan Luo and Qiancai Wang and Xinyuan Yu and Haonan Chen and Cewu Lu and Lin Shao},
booktitle={IEEE/CVF International Conference on Computer Vision (ICCV)},
year={2023},
}