This is the code of pytorch version for our IROS2019 paper and TASE2021 journal paper: PPR-Net: point-wise pose regression network for instance segmentation and 6d pose estimation in bin-picking scenarios; PPR-Net++: Accurate 6D Pose Estimation in Stacked Scenarios.
Ubuntu 16.04/18.04
python3.6, torch 1.1.0, torchvision 0.3.0, opencv-python, sklearn, h5py, nibabel, et al.
Our backbone PointNet++ is borrowed from pointnet2.
Siléane dataset is available at here.
Fraunhofer IPA Bin-Picking dataset is available at here.
The python code of evaluation metric is available at here.
If you use this codebase in your research, please cite:
@inproceedings{pprnet19IROS,
title={PPR-Net: point-wise pose regression network for instance segmentation and 6d pose estimation in bin-picking scenarios},
author={Dong, Zhikai and Liu, Sicheng and Zhou, Tao and Cheng, Hui and Zeng, Long and Yu, Xingyao and Liu, Houde},
booktitle={2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
pages={1773--1780},
year={2019},
organization={IEEE}
}
@article{zeng2021ppr,
title={PPR-Net++: accurate 6-D pose estimation in stacked scenarios},
author={Zeng, Long and Lv, Wei Jie and Dong, Zhi Kai and Liu, Yong Jin},
journal={IEEE Transactions on Automation Science and Engineering},
volume={19},
number={4},
pages={3139--3151},
year={2021},
publisher={IEEE}
}