Volume 43 Issue 4
May  2014
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Wang Zhaofeng, Yan Bin, Tong Li, Chen Jian, Li Jianxin. Normal estimate method of point clouds based on adaptive neighbor size[J]. Infrared and Laser Engineering, 2014, 43(4): 1322-1326.
Citation: Wang Zhaofeng, Yan Bin, Tong Li, Chen Jian, Li Jianxin. Normal estimate method of point clouds based on adaptive neighbor size[J]. Infrared and Laser Engineering, 2014, 43(4): 1322-1326.

Normal estimate method of point clouds based on adaptive neighbor size

  • Received Date: 2013-08-20
  • Rev Recd Date: 2013-09-28
  • Publish Date: 2014-04-25
  • Normal vector estimation in three-dimensional space is of great significance in the field of research in computer vision and surface reconstruction, the local surface fitting method is a classical estimation method of point cloud data. In order to improve the veracity of the normal vector which computed by the way of local surface fitting, a method based on optimal neighborhood size for normals estimation was described and analyzed in this paper. The distribution of the neighbor of every point was formulated on the basis of the projection of gradient. Then, the adaptive size was chosen based on the distribution of the neighbor, the normal vector was fitted by the adaptive size. Experimental results show that presented algorithm could avoid the radius of neighbor estimated too large or too small, improve the veracity of the normal vector which computed by the way of local surface fitting effectively.
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    [21] Wang Zhaofeng, Yan Bin, Li Jianxin, et al. A new method of point clouds extraction from the 3D volume data [C]// International Conference on Nature Computation, 2012: 369-372.
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Normal estimate method of point clouds based on adaptive neighbor size

  • 1. Information Systems Engineering College,Information Engineering University,Zhengzhou 450002,China

Abstract: Normal vector estimation in three-dimensional space is of great significance in the field of research in computer vision and surface reconstruction, the local surface fitting method is a classical estimation method of point cloud data. In order to improve the veracity of the normal vector which computed by the way of local surface fitting, a method based on optimal neighborhood size for normals estimation was described and analyzed in this paper. The distribution of the neighbor of every point was formulated on the basis of the projection of gradient. Then, the adaptive size was chosen based on the distribution of the neighbor, the normal vector was fitted by the adaptive size. Experimental results show that presented algorithm could avoid the radius of neighbor estimated too large or too small, improve the veracity of the normal vector which computed by the way of local surface fitting effectively.

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