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Self-organizing Extraction Reconstruction of Topologic Rectangular Mesh for Dense 3-D Scattered Data

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

Issue:
2005年02期
Page:
136-139
Research Field:
Publishing date:

Info

Title:
Self-organizing Extraction Reconstruction of Topologic Rectangular Mesh for Dense 3-D Scattered Data
Author(s):
ZHANG Wei 1JIANG Xian-feng 2SUN Yi 2DING Qiu-lin 3
1.Department of Mechanical and Electronic Engineering, Zhejiang University City College, Hangzhou 310015, China;2. College of Mechanical and Electronic Engineering,Zhejiang University of Technology, Hangzhou 310014, China;3. College of Information Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
Keywords:
reverse engineering rectangular mesh neural netw ork scattered point s data ex traction
PACS:
TP391.7
DOI:
-
Abstract:
Based on the sel-f organizing feature map( SOFM) neural network, an approach is developed to extract the dense 3-D scattered data and to produce the topologic rectangular mesh. The inherent topologic relations between the scattered points on the curved surface are reconstructed by the weight vectors of the neurons on the output layer of the neural network. The weight vectors of the neurons on the output layer of the neural network are used to approximate the dense 3-D scattered points, so the dense scattered points can be reduced to the reasonable scale, while the topologic feature of the whole scat tered points remained. The region of the lateral inhibition is rectangle within which the neuron weight vectors are adjusted according to the SOFM training algorithm. The neurons on the output layer are distributed in the array of rectangle after t raining, thereby the topologic rectangular mesh in a way of high approximation is produced which can be used to reconstruct the surface with NURBS method. The computer simulation results show that this approach is satisfactory.

References:

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Memo

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Last Update: 2013-05-23