Journal of Jilin University(Engineering and Technology Edition) ›› 2022, Vol. 52 ›› Issue (12): 2971-2983.doi: 10.13229/j.cnki.jdxbgxb20210526

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Dense correspondence calculation of 3D models based on unsupervised deformed network

Jun YANG1,2(),Zhi-ming GAO1   

  1. 1.School of Electronic and Information Engineering，Lanzhou Jiaotong University，Lanzhou 730070，China
    2.Faculty of Geomatics，Lanzhou Jiaotong University，Lanzhou 730070，China
  • Received:2021-06-18 Online:2022-12-01 Published:2022-12-08

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Abstract:

Aiming at the poor generalization ability of dense correspondence computation algorithms of the 3D shapes using deep learning， a method for computing correspondence based on unsupervised deformable networks was proposed. Firstly， the local feature descriptors of non-parametric templates were extracted by the template feature extraction network based on spiral convolution. Secondly， the global features of the input shape were obtained through the encoder in the template deformation network， and spliced with the local feature descriptors of the template. Then the joined features were input into the decoder to calculate the deformation model of the template. Finally， the dense correspondence between the deformation model and the input model was calculated by the nearest neighbor search algorithm. The experiment results show that the proposed algorithm can obtain more accurate dense correspondence under the same training dataset and has a stronger generalization ability than that of the current mainstream algorithms.

Key words: computer application, correspondence, model deformation, geometric deep learning, unsupervised, graph convolutional neural network

CLC Number: 

  • TP391.4

Fig.1

Spiral sequence"

Fig.2

Template feature extraction network"

Fig.3

Template deformation network"

Fig.4

Chamfer distance"

Fig.5

Laplacian operator"

Fig.6

Different human body and posturein the FAUST dataset"

Table 1

Comparison of running time of different algorithms"

算法预处理时间/s

训练和测试

时间/s

总运行时间/s
3D-CODED1250005062
DFMNet7015001570
SURFMNet153570
Unsup FMNet154580
本文12198260

Fig.7

Comparison between our method and other methods for constructing correspondence on FAUST dataset"

Fig.8

Comparison between our method and other methods for texture transfer on FAUST dataset"

Fig.9

Comparison between our method and other methods for constructing correspondence on CoMa dataset"

Fig.10

Test results of our algorithm on the SMAL5K dataset"

Fig.11

Geodetic error curve"

Table 2

Average geodetic error of different algorithms"

算法FAUST数据集CoMa数据集
SURFMNet0.320.29
Unsup FMNet0.220.30
DFMNet0.330.54
3D-CODED0.330.26
本文0.150.09
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