Density-Based Shape Descriptors for 3D Object Retrieval

  • Ceyhun Burak Akgül
  • Bülent Sankur
  • Francis Schmitt
  • Yücel Yemez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4105)


We develop a probabilistic framework that computes 3D shape descriptors in a more rigorous and accurate manner than usual histogram-based methods for the purpose of 3D object retrieval. We first use a numerical analytical approach to extract the shape information from each mesh triangle in a better way than the sparse sampling approach. These measurements are then combined to build a probability density descriptor via kernel density estimation techniques, with a rule-based bandwidth assignment. Finally, we explore descriptor fusion schemes. Our analytical approach reveals the true potential of density-based descriptors, one of its representatives reaching the top ranking position among competing methods.


Discount Cumulative Gain Bandwidth Matrix Princeton Shape Benchmark Local Shape Feature Local Geometric Feature 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ceyhun Burak Akgül
    • 1
    • 2
  • Bülent Sankur
    • 1
  • Francis Schmitt
    • 2
  • Yücel Yemez
    • 3
  1. 1.Department of Electrical and Electronics EngineeringBoğaziçi UniversityIstanbulTurkey
  2. 2.GET-Télécom – CNRS UMRFrance
  3. 3.Department of Computer EngineeringKoç UniversityIstanbulTurkey

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