A 3D shape classifier with neural network supervision

  • Authors:

  • Zhenbao Liu;Jun Mitani;Yukio Fukui;Seiichi Nishihara

  • Affiliations:

  • Department of Computer Science, Graduate School of Systems and Information Engineering, University of Tsukuba, Tennodai 1-1-1, Tsukuba, Ibaraki, 305-8573, Japan.;Department of Computer Science, Graduate School of Systems and Information Engineering, University of Tsukuba, Tennodai 1-1-1, Tsukuba, Ibaraki, 305-8573, Japan.;Department of Computer Science, Graduate School of Systems and Information Engineering, University of Tsukuba, Tennodai 1-1-1, Tsukuba, Ibaraki, 305-8573, Japan.;Department of Computer Science, Graduate School of Systems and Information Engineering, University of Tsukuba, Tennodai 1-1-1, Tsukuba, Ibaraki, 305-8573, Japan

  • Venue:
  • International Journal of Computer Applications in Technology
  • Year:
  • 2010

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Abstract

The task of 3D shape classification is to assign a set of unordered shapes into pre-tagged classes with class labels. In this paper, we present a 3D shape classifier approach based on supervision of the learning of point spatial distributions. We first extract the low-level features by characterising the point spatial density distributions, and train one feed-forward neural network to learn these features by examples. The Konstanz shape database was chosen as the test database to evaluate the accuracy rate of classification. We also compared this classifier to the k nearest neighbours classifier for 3D shapes.