A Genetic Algorithm-Evolved 3D Point Cloud Descriptor

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


In this paper we propose a new descriptor for 3D point clouds that is fast when compared to others with similar performance and its parameters are set using a genetic algorithm. The idea is to obtain a descriptor that can be used in simple computational devices, that have no GPUs or high computational capabilities and also avoid the usual time-consuming task of determining the optimal parameters for the descriptor. Our proposal is compared with other similar algorithms in a public available point cloud library (PCL [1]). We perform a comparative evaluation on 3D point clouds using both the object and category recognition performance. Our proposal presents a comparable performance with other similar algorithms but is much faster and requires less disk space.


Genetic Algorithm Point Cloud Color Histogram Good Chromosome Shape Histogram 
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

  • Dominik Węgrzyn
    • 1
  • Luís A. Alexandre
    • 1
  1. 1.IT - Instituto de Telecomunicações, Dept. of Computer ScienceUniv. Beira InteriorCovilhãPortugal

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