3D Zernike Descriptors for Content Based Shape Retrieval

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@INPROCEEDINGS{novotni-2003-3d,
       author = {Novotni, Marcin and Klein, Reinhard},
        title = {3D Zernike Descriptors for Content Based Shape Retrieval},
    booktitle = {The 8th ACM Symposium on Solid Modeling and Applications},
         year = {2003},
        month = jun,
  institution = {Universit{\"a}t Bonn},
     abstract = {Content based 3D shape retrieval for broad domains like the World
                 Wide Web has recently gained considerable attention in Computer
                 Graphics community. One of the main challenges in this context
                 is the mapping of 3D objects into compact canonical representations
                 referred to as descriptors, which serve as search keys during
                 the retrieval process. The descriptors should have certain desirable
                 properties like invariance under scaling, rotation and translation.
                 Very importantly, they should possess descriptive power providing
                 a basis for similarity measure between three-dimensional objects
                 which is close to the human notion of resemblance.
                 In this paper we advocate the usage of so-called 3D Zernike invariants
                 as descriptors for content based 3D shape retrieval. The
                 basis polynomials of this representation facilitate computation of
                 invariants under the above transformations. Some theoretical results
                 have already been summarized in the past from the aspect of
                 pattern recognition and shape analysis. We provide practical analysis
                 of these invariants along with algorithms and computational
                 details. Furthermore, we give a detailed discussion on influence
                 of the algorithm parameters like type and resolution of the conversion
                 into a volumetric function, number of utilized coefficients, etc.
                 As is revealed by our study, the 3D Zernike descriptors are natural
                 extensions of spherical harmonics based descriptors, which are reported
                 to be among the most successful representations at present.
                 We conduct a comparison of 3D Zernike descriptors against these
                 regarding computational aspects and shape retrieval performance.},
   conference = {The 8th ACM Symposium on Solid Modeling and Applications, June 16-20, Seattle, WA}
}