耦合映射的非等距三维模型簇对应关系计算

doi:10.16451/j.cnki.issn1003-6059.202502002

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Abstract To address the issues of low accuracy and poor generalization ability in existing non-isometric 3D shape collection correspondence calculation methods, a correspondence calculation method for non-isometric 3D shape collection via coupled maps is proposed. First, DiffusionNet is employed to directly extract initial features from the 3D shape, and thus discriminative feature descriptors are obtained. Then, functional maps matrix and point-to-point maps matrix are computed using these descriptors. Structural regularization constraints and softmax normalization are applied to both matrices, respectively, to obtain an optimal coupled maps matrix. Finally, a shape collection matching module based on a virtual template takes the initial model features as input and employs a point classifier constructed with the coupled maps to directly predict the correspondence between the shapes and the virtual templates. The final correspondence for the non-isometric shape collection is obtained through Gumbel-Sinkhorn normalization. Experimental results demonstrate that the proposed method effectively handles topological noise within non-isometric shapes, achieves low geodesic error in correspondence calculation, provides accurate results, and exhibits strong generalization ability.

Key words： Shape Correspondence Non-Isometric 3D Shape Collection Deep Learning Coupled Maps Gumbel-Sinkhorn Normalization

Received: 25 November 2024

ZTFLH:

TP391.4

Fund:National Natural Science Foundation of China(No.42261067), 2025 Key Talent Project of Gansu Province(No.2025RCXM031)

Corresponding Authors: YANG Jun, Ph.D., professor. His research interests include computer graphics, remote sensing image analysis and processing, and deep learning.

About author:: XUE Youzhong, Master student. His research interests include 3D shape correspondence and deep learning.

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Cite this article:

YANG Jun,XUE Youzhong. Correspondence Calculation for Non-isometric 3D Shape Collection via Coupled Maps[J]. Pattern Recognition and Artificial Intelligence, 2025, 38(2): 116-131.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202502002 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2025/V38/I2/116

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