Point Pair Feature Matching: Evaluating Methods to Detect Simple Shapes

Research output: Chapter in Book/Report/Conference proceedingConference contribution


External organisations

  • Technical University Berlin


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.


Original languageEnglish
Title of host publicationComputer Vision Systems - 12th International Conference, ICVS 2019, Proceedings
EditorsDimitrios Tzovaras, Dimitrios Giakoumis, Markus Vincze, Antonis Argyros
Publication statusPublished - 2019
Externally publishedYes
Event12th International Conference on Computer Vision Systems, ICVS 2019 - Thessaloniki, Greece
Duration: 23 Sep 201925 Sep 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11754 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference12th International Conference on Computer Vision Systems, ICVS 2019


  • Object detection, Point Pair Features, Pose estimation