Computer Science ›› 2018, Vol. 45 ›› Issue (6): 265-269.doi: 10.11896/j.issn.1002-137X.2018.06.047

• Graphics, Image & Pattern Recognition • Previous Articles     Next Articles

3D Geometric Reconstruction Based on Bayes-MeTiS Mesh Partition

ZHANG Xiao-hua, HUANG Bo   

  1. College of Computer Science,Sichuan University,Chengdu 611844,China
  • Received:2017-04-20 Online:2018-06-15 Published:2018-07-24

Abstract: In order to improve the compression efficiency of the geometric reconstruction process of 3D model,this paper proposed a bayesian geometric reconstruction algorithm based on MeTiS mesh partition for 3D model.At the encoding part,the MeTiS method is used to realize the subnetting for original 3D grid,the random linear matrix is used to encode the geometry of the subnet,and the pseudo random number generator is used for data sequence construction by considering the neighbor nodes of the boundary nodes.Then,the Bayesian algorithm is used to design the geometric model reconstruction algorithm,and the mean,variance matrix and the model parameters are given in theory to realize the geometric reconstruction of the 3D model.Finally,by comparing with graph Fourier transform spectral compression(GFT),least square compression(LMS) and compressed sensing based graph Fourier transform spectral compression algorithms(CSGFT),the simulation results show that the proposed method has relatively high bit rate compression index and low reconstruction error.

Key words: 3D model, Geometric reconstruction, MeTiS mesh partition, Bias, Neighbor node

CLC Number: 

  • TP391.2
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