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Occlusion Resistant Object Rotation Regression from Point Cloud Segments

  • Ge GaoEmail author
  • Mikko Lauri
  • Jianwei Zhang
  • Simone Frintrop
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11129)

Abstract

Rotation estimation of known rigid objects is important for robotic applications such as dexterous manipulation. Most existing methods for rotation estimation use intermediate representations such as templates, global or local feature descriptors, or object coordinates, which require multiple steps in order to infer the object pose. We propose to directly regress a pose vector from point cloud segments using a convolutional neural network. Experimental results show that our method achieves competitive performance compared to a state-of-the-art method, while also showing more robustness against occlusion. Our method does not require any post processing such as refinement with the iterative closest point algorithm.

Keywords

6D pose estimation Convolutional neural network Point cloud Lie algebra 

Notes

Acknowledgments

This work was partially funded by the German Science Foundation (DFG) in project Crossmodal Learning, TRR 169.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of InformaticsUniversity of HamburgHamburgGermany

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