Point Pair Feature Matching: Evaluating Methods to Detect Simple Shapes

  • Markus ZieglerEmail author
  • Martin Rudorfer
  • Xaver Kroischke
  • Sebastian Krone
  • Jörg Krüger
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11754)


A recent benchmark for 3D object detection and 6D pose estimation from RGB-D images shows the dominance of methods based on Point Pair Feature Matching (PPFM). Since its invention in 2010 several modifications have been proposed to cope with its weaknesses, which are computational complexity, sensitivity to noise, and difficulties in the detection of geometrically simple objects with planar surfaces and rotational symmetries. In this work we focus on the latter. We present a novel approach to automatically detect rotational symmetries by matching the object model to itself. Furthermore, we adapt methods for pose verification and use more discriminative features which incorporate global information into the Point Pair Feature. We also examine the effects of other, already existing extensions by testing them on our specialized dataset for geometrically primitive objects. Results show that particularly our handling of symmetries and the augmented features are able to boost recognition rates.


Object detection Pose estimation Point Pair Features 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Markus Ziegler
    • 1
    Email author
  • Martin Rudorfer
    • 1
  • Xaver Kroischke
    • 1
  • Sebastian Krone
    • 1
  • Jörg Krüger
    • 1
  1. 1.Technische Universität BerlinBerlinGermany

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