Abstract
A Robust and Informative Local Shape Descriptor Plays an Important Role in Mesh Registration. in This Regard, Spectral Descriptors that Are based on the Spectrum of the Laplace-Beltrami Operator Have Been a Popular Subject of Research for the Last Decade Due to their Advantageous Properties, Such as Isometry Invariance. Despite Such, However, Spectral Descriptors Often Fail to Give a Correct Similarity Measure for Nonisometric Cases Where the Metric Distortion between the Models is Large. Hence, They Are Not Reliable for Correspondence Matching Problems When the Models Are Not Isometric. in This Paper, it is Proposed a Method to Improve the Similarity Metric of Spectral Descriptors for Correspondence Matching Problems. We Embed a Spectral Shape Descriptor into a Different Metric Space Where the Euclidean Distance between the Elements Directly Indicates the Geometric Dissimilarity. We Design and Train a Siamese Neural Network to Find Such an Embedding, Where the Embedded Descriptors Are Promoted to Rearrange based on the Geometric Similarity. We Demonstrate Our Approach Can Significantly Enhance the Performance of the Conventional Spectral Descriptors by the Simple Augmentation Achieved Via the Siamese Neural Network in Comparison to Other State-Of-The-Art Methods.
Recommended Citation
Z. Sun et al., "Embedded Spectral Descriptors: Learning the Point-Wise Correspondence Metric Via Siamese Neural Networks," Journal of Computational Design and Engineering, vol. 7, no. 1, pp. 18 - 29, Oxford University Press, Feb 2020.
The definitive version is available at https://doi.org/10.1093/jcde/qwaa003
Department(s)
Engineering Management and Systems Engineering
Publication Status
Open Access
Keywords and Phrases
Siamese neural networks; spectral shape descriptor; point-wise correspondence
International Standard Serial Number (ISSN)
2288-5048; 2288-4300
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2024 Oxford University Press, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution 4.0 License.
Publication Date
01 Feb 2020
