Set Distance Functions for 3D Object Recognition

  • Luís A. Alexandre
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8258)

Abstract

One of the key steps in 3D object recognition is the matching between an input cloud and a cloud in a database of known objects. This is usually done using a distance function between sets of descriptors. In this paper we propose to study how several distance functions (some already available and other new proposals) behave experimentally using a large freely available household object database containing 1421 point clouds from 48 objects and 10 categories. We present experiments illustrating the accuracy of the distances both for object and category recognition and find that simple distances give competitive results both in terms of accuracy and speed.

Keywords

Distance Function Point Cloud Object Recognition Local Reference Frame Simple Distance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Luís A. Alexandre
    • 1
  1. 1.Instituto de TelecomunicaçõesUniv. Beira InteriorCovilhãPortugal

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