Shape Measure for Identifying Perceptually Informative Parts of 3D Objects

  • Authors:

  • Sreenivas Sukumar;David Page;Andrei Gribok;Andreas Koschan;Mongi Abidi

  • Affiliations:

  • The University of Tennessee, USA;University of Alabama in Huntsville, USA;University of Alabama in Huntsville, USA;University of Alabama in Huntsville, USA;University of Alabama in Huntsville, USA

  • Venue:
  • 3DPVT '06 Proceedings of the Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06)
  • Year:
  • 2006

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Abstract

We propose a mathematical approach for quantifying shape complexity of 3D surfaces based on perceptual principles of visual saliency. Our curvature variation measure (CVM), as a 3D feature, combines surface curvature and information theory by leveraging bandwidth-optimized kernel density estimators. Using a part decomposition algorithm for digitized 3D objects, represented as triangle meshes, we apply our shape measure to transform the low level mesh representation into a perceptually informative form. Further, we analyze the effects of noise, sensitivity to digitization, occlusions, and descriptiveness to demonstrate our shape measure on laser-scanned real world 3D objects.