Leveraging Symmetries to Improve Object Detection and Pose Estimation from Range Data

  • Sergey V. AlexandrovEmail author
  • Timothy Patten
  • Markus Vincze
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11754)


Many man-made objects around us exhibit rotational symmetries. This fact can be exploited to improve object detection and 6D pose estimation performance. To this end we propose a set of extensions to the state-of-the-art PPF pipeline. We describe how a fundamental region is selected on symmetrical objects and used to construct a compact model hash table and a Hough voting space without redundancies. We also introduce a symmetry-aware distance metric for the pose clustering step. Our experiments on T-LESS and ToyotaLight datasets demonstrate that these extensions lead to a consistent improvement in the pose estimation recall score compared to the baseline pipeline, while simultaneously reducing computation time by up to 4 times.



The work presented in this paper has been partially supported by Aeolus Robotics, Inc.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Sergey V. Alexandrov
    • 1
    Email author
  • Timothy Patten
    • 1
  • Markus Vincze
    • 1
  1. 1.Vision4Robotics Group, ACINTU WienViennaAustria

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