Shape-Based Retrieval of Articulated 3D Models Using Spectral Embedding

  • Varun Jain
  • Hao Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4077)


We present an approach for robust shape retrieval from data-bases containing articulated 3D shapes. We represent each shape by the eigenvectors of an appropriately defined affinity matrix, obtaining a spectral embedding. Retrieval is then performed on these embeddings using global shape descriptors. Transformation into the spectral domain normalizes the shapes against articulation (bending), rigid-body transformations, and uniform scaling. Experimentally, we show absolute improvement in retrieval performance when conventional shape descriptors are used in the spectral domain on the McGill database of articulated 3D shapes. We also propose a simple eigenvalue-based descriptor, which is easily computed and performs comparably against the best known shape descriptors applied to the original shapes.


Geodesic Distance Shape Descriptor Shape Retrieval Princeton Shape Benchmark Shape Database 


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© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Varun Jain
    • 1
  • Hao Zhang
    • 1
  1. 1.GrUVi Lab, School of Computing SciencesSimon Fraser UniversityBurnaby, British ColumbiaCanada

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