A direct recovery of superquadric models in range images using recover-and-select paradigm

  • Aleš Leonardis
  • Franc Solina
  • Alenka Macerl
Shape Modelling
Part of the Lecture Notes in Computer Science book series (LNCS, volume 800)


We present a novel approach to reliable and efficient recovery of part-descriptions from range images. We show that a set of superquadric models can be directly recovered from unsegmented range data, as opposed to methods which attempt the recovery of volumetric models only after the data has been pre-segmented using extensive pre-processing. The approach is based on the recover-and-select paradigm which consists of two intertwined stages: model-recovery and model-selection. At the model-recovery stage a redundant set of superquadrics is initiated in the image and allowed to grow, which involves an iterative procedure combining data classification and parameter estimation. All the recovered models are passed to the model-selection procedure where only the models resulting in the simplest overall description are selected.


Computer Vision Range Image Model Recovery Volumetric Model Final Description 
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 1994

Authors and Affiliations

  • Aleš Leonardis
    • 1
  • Franc Solina
    • 1
  • Alenka Macerl
    • 1
  1. 1.Computer Vision Laboratory, Faculty of Electrical Engineering and Computer ScienceUniversity of LjubljanaLjubljanaSlovenia

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