SLPRNet: A 6D Object Pose Regression Network by Sample Learning

Zheng Zhang, Xingru Zhou, Houde Liu

Abstract

Visual grasping holds important implications for robot manipulation situations. As a core procedure in such grasping tasks, pose regression has attracted lots of research attention, among which point cloud based deep learning methods achieve relatively better result. The usual backbone of such network architectures includes sampling, grouping and feature extracting processes. We argue that common sampling techniques like Farthest Point Sampling(FPS), Random Sampling(RS) and Geometry Sampling(GS) hold potential defectiveness. So we devise a pre-posed network which aims at learning to sample the most suitable points in the whole point cloud for a downstream pose regression task and show its superiority comparing to the above-mentioned sampling methods. In conclusion, we propose a Sample Learning Pose Regression network (SLPRNet) to regress each instances pose in a standard grasping situation. Meanwhile, we build a point cloud dataset to train and test our network. In experiment, we reach an average precision(AP) up to 89.8% on dataset generated from Silane and an average distance(ADD) up to 91.0% on YCB. Real-world grasp experiments also verify the validity of our work.

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Paper Citation


in Harvard Style

Zhang Z., Zhou X. and Liu H. (2021). SLPRNet: A 6D Object Pose Regression Network by Sample Learning.In Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-484-8, pages 1233-1240. DOI: 10.5220/0010385912331240


in Bibtex Style

@conference{icaart21,
author={Zheng Zhang and Xingru Zhou and Houde Liu},
title={SLPRNet: A 6D Object Pose Regression Network by Sample Learning},
booktitle={Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2021},
pages={1233-1240},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010385912331240},
isbn={978-989-758-484-8},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - SLPRNet: A 6D Object Pose Regression Network by Sample Learning
SN - 978-989-758-484-8
AU - Zhang Z.
AU - Zhou X.
AU - Liu H.
PY - 2021
SP - 1233
EP - 1240
DO - 10.5220/0010385912331240