E2EK : End-to-End Regression Network Based on Keypoint for 6D Pose Estimation

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

17 Scopus Citations
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Author(s)

  • Shifeng Lin
  • Zunran Wang
  • Yonggen Ling
  • Yidan Tao
  • Chenguang Yang

Detail(s)

Original languageEnglish
Pages (from-to)6526-6533
Journal / PublicationIEEE Robotics and Automation Letters
Volume7
Issue number3
Online published11 May 2022
Publication statusPublished - Jul 2022

Abstract

The methods based on deep learning are the mainstream of 6D object pose estimation, which mainly include direct regression and two-stage pipelines. The former are keen by many scholarsat first due to their simplicity and differentiability to poses, but they usually lack in accuracy when compared with the latter that estimate the intermediate variables relating to geometries such as object keypoints or 2D-3D correspondence before PnP/RANSAC algorithm. However, the loss function of the two-stage method is non-differentiable to the 6D pose, which is hard to apply in the tasks requiring the differentiable poses. To overcome the disadvantages of the above methods, we propose an end-to-end regression network based on keypoints for 6D pose estimation. Specifically, we supervise the point-wise key point offsets that help the network to learn the geometric information and directly regress the 6D pose through aggregating keypoints to achieve differentiability to the pose. Furthermore, we improve the sampling method by sampling points around objects that benefits the small object and design a unit loss function that helps the learning of the keypoints. Experimental results show that our approach outperforms most methods on LM, LM-O and YCB-V datasets.

Research Area(s)

  • Deep learning for visual perception, pose estimation, RGB-D perception, ROBUST

Citation Format(s)

E2EK: End-to-End Regression Network Based on Keypoint for 6D Pose Estimation. / Lin, Shifeng; Wang, Zunran; Ling, Yonggen et al.
In: IEEE Robotics and Automation Letters, Vol. 7, No. 3, 07.2022, p. 6526-6533.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review