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Encyclopedia of Database Systems pp 1467–1471Cite as

Feature-Based 3D Object Retrieval

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Synonyms

Shape descriptors; Three-dimensional similarity search

Definition

3D objects are an important type of data with many applications in domains such as Engineering and Computer Aided Design, Science, Simulation, Visualization, Cultural Heritage, and Entertainment. Technological progress in acquisition, modeling, processing, and dissemination of 3D geometry leads to the accumulation of large repositories of 3D objects. Consequently, there is a strong need to research and develop technology to support the effective retrieval of 3D object data from 3D repositories.

The feature-based approach is a prominent technique to implement content-based retrieval functionality for 3D object databases. It relies on extracting characteristic numerical attributes (so-called features) from a 3D object. These are often encoded as high-dimensional vectors which represent either the 3D object (global feature vector), or parts of it (local feature vectors). The 3D feature vectors in turn are used to...

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Recommended Reading

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Chile, Santiago, Chile

    Benjamin Bustos

  2. Department of Computer Science and Biomedical Engineering, Institute of Computer Graphics and Knowledge Visualization, Graz University of Technology, Graz, Austria

    Tobias Schreck

Authors
  1. Benjamin Bustos
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  2. Tobias Schreck
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Corresponding author

Correspondence to Benjamin Bustos .

Editor information

Editors and Affiliations

  1. Georgia Institute of Technology College of Computing, Atlanta, GA, USA

    Ling Liu

  2. University of Waterloo School of Computer Science, Waterloo, ON, Canada

    M. Tamer Özsu

Section Editor information

  1. The Chinese University of Hong Kong, Hong Kong, China

    Jeffrey Xu Yu

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Bustos, B., Schreck, T. (2018). Feature-Based 3D Object Retrieval. In: Liu, L., Özsu, M.T. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8265-9_161

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