Object Pose Estimation Incorporating Projection Loss and Discriminative Refinement

Jiun Kai You, Chen Chien James Hsu, Wei Yen Wang, Shao Kang Huang

Research output: Contribution to journalArticlepeer-review

Abstract

The accurate estimation of three-dimensional (3D) object pose is important in a wide range of applications, such as robotics and augmented reality. The key to estimate object poses is matching feature points in the captured image with predefined ones of the 3D model of the object. Existing learning-based pose estimation systems utilize a voting strategy to estimate the feature points in a vector space for improving the accuracy of the estimated pose. However, the loss function of such approaches only takes account of the direction of the vector, resulting in an error-prone localization of feature points. Therefore, this paper considers a projection loss function dealing with the error of the vector field and incorporates a refinement network to revise the predicted pose to obtain a good final output. Experimental results show that the proposed methods outperform the state-of-the-art methods in ADD(-S) metric on the LINEMOD and Occlusion LINEMOD datasets. Moreover, the proposed method can be applied to real-world practical scenarios in real time to simultaneously estimate the poses of multiple objects.

Original languageEnglish
Article number9335573
Pages (from-to)18597-18606
Number of pages10
JournalIEEE Access
Volume9
DOIs
Publication statusPublished - 2021

Keywords

  • LINEMOD
  • Object pose estimation
  • convolutional neural network
  • deep learning
  • occlusion LINEMOD

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

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