3D Model Retrieval Using the Histogram of Orientation of Suggestive Contours

  • Sang Min Yoon
  • Arjan Kuijper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6939)


The number of available 3D models in various areas increases steadily. Efficient methods to search for 3D models by content, rather than textual annotations, are crucial. For this purpose, we propose a content based 3D model retrieval system using the Histogram of Orientation (HoO) from suggestive contours and their diffusion tensor fields. Our approach to search and automatically return a set of 3D mesh models from a large database consists of three major steps: (1) suggestive contours extraction from different viewpoints to extract features of the query 3D model; (2) HoO descriptor computation by analyzing the diffusion tensor fields of the suggestive contours; (3) similarity measurement to retrieve the models and the most probable view-point. Our proposed 3D model retrieval system is very efficient to retrieve the 3D models even though there are variations of shape and pose of the models. Experimental results are presented and indicate the effectiveness of our approach, competing with the current – more complicated – state of the art method and even improving results for several classes.


Diffusion Tensor Query Model Contour Pixel Suggestive Contour Diffusion Tensor Field 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Sang Min Yoon
    • 1
  • Arjan Kuijper
    • 2
  1. 1.Digital Human Research CenterAISTTokyoJapan
  2. 2.Fraunhofer IGDDarmstadtGermany

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