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Computational Methods for Selective Acquisition of Depth Measurements: An Experimental Evaluation

  • Pierre Payeur
  • Phillip Curtis
  • Ana-Maria Cretu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8192)

Abstract

Acquisition of depth and texture with vision sensors finds numerous applications for objects modeling, man-machine interfaces, or robot navigation. One challenge resulting from rich textured 3D datasets resides in the acquisition, management and processing of the large amount of data generated, which often preempts full usage of the information available for autonomous systems to make educated decisions. Most subsampling solutions to reduce dataset’s dimension remain independent from the content of the model and therefore do not optimize the balance between the richness of the measurements and their compression. This paper experimentally evaluates the performance achieved with two computational methods that selectively drive the acquisition of depth measurements over regions of a scene characterized by higher 3D features density, while capitalizing on the knowledge readily available in previously acquired data. Both techniques automatically establish which subsets of measurements contribute most to the representation of the scene, and prioritize their acquisition. The algorithms are validated on datasets acquired from two different RGB-D sensors.

Keywords

3D imaging depth measurement RGB-D cameras computational intelligence selective sampling neural gas 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Pierre Payeur
    • 1
  • Phillip Curtis
    • 1
  • Ana-Maria Cretu
    • 2
  1. 1.School of Electrical Engineering and Computer ScienceUniversity of OttawaOttawaCanada
  2. 2.Department of Computer Science and EngineeringUniversité du Québec en OutaouaisGatineauCanada

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