Extended cone-curvature based salient points detection and 3D model retrieval

  • Authors:
  • Yujie Liu;Xiaodong Zhang;Zongmin Li;Hua Li

  • Affiliations:
  • School of Computer Science and Communication Engineering, China University of Petroleum, Qingdao, China 266555;Center for Human Computer Interaction, Shenzhen Institute of Advanced Integration Technology, Qingdao, China 266555;School of Computer Science and Communication Engineering, China University of Petroleum, Qingdao, China 266555;National Research Center for Intelligent Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China 100190

  • Venue:
  • Multimedia Tools and Applications
  • Year:
  • 2013

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Abstract

Local feature extraction of 3D model has become a more and more important aspect in terms of 3D model shape feature extraction. Compared with the global feature, it is more suitable to do the partial retrieval and more robust to the model deformation. In this paper, a local feature called extended cone-curvature feature is proposed to describe the local shape feature of 3D model mesh. Based on the extended cone-curvature feature, salient points and salient regions are extracted by using a new salient point detection method. Then extended cone-curvature feature and local shape distribution feature calculated on the salient regions are used together as shape index, and the earth mover's distance is employed to accomplish similarity measure. After many times' retrieval experiments, the new extended cone-curvature descriptor we propose has more efficient and effective performance than shape distribution descriptor and light field descriptor especially on deformable model retrieval.