3D Model Retrieval Using 2D View and Transform-Based Features

  • Pengjie Li
  • Huadong Ma
  • Anlong Ming
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6297)


In this paper, we propose a novel hybrid shape descriptor that combines the 2D view and transform-based features. The 2D view features are extracted from six orthogonal directions of a 3D model by using the Scale Invariant Feature Transform (SIFT) method. In order to capture the six orthogonal 2D views, Continuous Principal Component Analysis (CPCA) is used to implement pose alignment. Meanwhile, the eigenspace is computed and stored to reduce the 2D view feature vector dimension. Then, the radial integral transform and the spherical integration transform are used to extract transform-based features. The similarity between the query model and models in the database is computed by using the weighted sum of 2D view and transform-based feature similarity. Experimental results show that the proposed hybrid shape descriptor can achieve satisfactory retrieval performance for both the articulated models in the McGill Shape Benchmark and the rigid models in the Princeton Shape Benchmark.


3D model retrieval 2D view feature transform-based feature hybrid shape descriptor 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Pengjie Li
    • 1
  • Huadong Ma
    • 1
  • Anlong Ming
    • 1
  1. 1.Beijing Key Laboratory of Intelligent Telecommunications Software and MultimediaBeijing University of Posts and TelecommunicationsBeijingChina

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