Journal of South China University of Technology (Natural Science Edition) ›› 2013, Vol. 41 ›› Issue (5): 68-72,79.doi: 10.3969/j.issn.1000-565X.2013.05.011

• Computer Science & Technology • Previous Articles     Next Articles

Fuzzy Normal Vector Estimation Algorithm of Three-Dimension Point Clouds

Liu Yan-ju1,2 Zhang Yong-de1 Yang Bo2   

  1. 1.Intelligent Machine Institute,Harbin University of Science and Technology,Harbin 150080,Heilongjiang,China;2.Computer Center,Qiqihar University,Qiqihar 161006,Heilongjiang,China
  • Received:2012-10-16 Revised:2013-02-21 Online:2013-05-25 Published:2013-04-01
  • Contact: 张永德(1965-)，男，博士，教授，博士生导师，主要从事医用机器人、服务机器人和仿生机器人的相关理论和应用研究． E-mail:zhangyd@hrbust.edu.cn
  • About author:刘艳菊(1974-)，女，博士生，副教授，主要从事三维重建技术、人工智能算法及其应用研究．E-mail:15146692464@163．com
  • Supported by:

    国家自然科学基金资助项目( 51205093， 50675054) ; 黑龙江省教育厅项目( 12511599)

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Abstract:

Before a three-dimension reconstruction,the normal vector should be estimated because it may be unreliabledue to the error of getting point clouds. In this paper,a fuzzy normal vector estimation algorithm for the objectwith any shape is proposed after analyzing the existing estimation algorithms. In this algorithm,first,the k-nearestneighbor value and the curvature of the cloud point data are input into a fuzzy inference system.Next,the pointclouds are classified according to fuzzy inference rules,and the parts with thin or sharp features are distinguishedfrom the point clouds of model and are then estimated with a checker and with the attachment point algorithm.Finally,the proposed algorithm is evaluated by using a denture model with several kinds of point clouds.The resultsshow that the algorithm is of high estimation accuracy,simplicity and feasibility.

Key words: three-dimension point cloud, fuzzy inference, normal vector estimation, sharp feature, thin feature

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