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 keypoints. 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. Supplemental materials and dataset are available on our project webpage.
@article{arxiv.2308.09987,
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},
journal= {arXiv preprint arXiv:2308.09987},
year = {2023}
}
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IEEE/CVF International Conference on Computer Vision (ICCV) 2023