Correspondence Calculation of Non-isometric 3D Point Shapes Based on Smooth Attention and Spectral Up-sampling Refinement

YANG Jun; ZHANG Siyang; WU Yan

doi:10.11999/JEIT231180

Volume 46 Issue 8

Aug. 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(8): 3285-3294

YANG Jun, ZHANG Siyang, WU Yan. Correspondence Calculation of Non-isometric 3D Point Shapes Based on Smooth Attention and Spectral Up-sampling Refinement[J]. Journal of Electronics & Information Technology, 2024, 46(8): 3285-3294. doi: 10.11999/JEIT231180

Citation:

YANG Jun, ZHANG Siyang, WU Yan. Correspondence Calculation of Non-isometric 3D Point Shapes Based on Smooth Attention and Spectral Up-sampling Refinement[J]. Journal of Electronics & Information Technology, 2024, 46(8): 3285-3294. doi: 10.11999/JEIT231180

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Correspondence Calculation of Non-isometric 3D Point Shapes Based on Smooth Attention and Spectral Up-sampling Refinement

doi: 10.11999/JEIT231180 cstr: 32379.14.JEIT231180

YANG Jun^{1, 2
,
,},
ZHANG Siyang¹,
WU Yan³

1.
School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
2.
Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China
3.
School of big data and artificial intelligence, Fujian Polytechnic Normal University, Fuqing 350300, China

Funds: The National Natural Science Foundation of China(42261067)

Received Date: 2023-10-30
Rev Recd Date: 2024-07-10

Available Online: 2024-07-29

Publish Date: 2024-08-30

Abstract

Abstract

To address the problem that the correspondence calculation of non-isometric 3D point cloud shape is easily affected by large-scale distortions, which often leads to corresponding distortions, low accuracy, and poor smoothness, a new algorithm of shape correspondence calculation for non-isometric 3D point cloud is proposed, which combines smooth attention with spectral up-sampling refinement. Firstly, a smooth attention mechanism and a smooth perception module are designed using the geometric feature information of the surface on which the points are located to improve the perception ability of the features for non-rigid transformations in large-scale deformation areas. Secondly, the deep functional maps module is combined with smooth regularization constraints to improve the smoothness of the functional maps calculation results. Finally, the final point-by-point mapping result is obtained using a multi-resolution reconstruction method in the spectral up-sampling refinement module. Experimental results show that the proposed algorithm has the smallest geodesic error in the correspondence constructed on the FAUST, SCAPE, and SMAL datasets compared with existing algorithms. It can improve the smoothness and global accuracy of point-by-point mapping for shapes with large-scale deformation.
- Shape correspondence,
- Non-isometric 3D point cloud shape,
- Smooth attention mechanism,
- Functional maps,
- Spectral up-sampling refinement

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References(19)

References

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