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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.

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