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Pose-invariant matching for non-rigid 3D models using Isomap

The wide usage of 3D mesh models greatly increases the importance of an effective matching algorithm for them. In this paper, we propose a novel 3D model matching algorithm. Firstly, vertices on the input 3D mesh models are mapped to 1D space by employing Isomap. A pose-invariant feature set is then...

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Detalles Bibliográficos
Autores principales: Jin, Hairong, Huang, Haichao, Wang, Zhiqiang, Xie, Yuqing, Zhou, Xinyue, Huang, Liming, Hong, Zhouzhenyan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8926203/
https://www.ncbi.nlm.nih.gov/pubmed/35294440
http://dx.doi.org/10.1371/journal.pone.0264192
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author Jin, Hairong
Huang, Haichao
Wang, Zhiqiang
Xie, Yuqing
Zhou, Xinyue
Huang, Liming
Hong, Zhouzhenyan
author_facet Jin, Hairong
Huang, Haichao
Wang, Zhiqiang
Xie, Yuqing
Zhou, Xinyue
Huang, Liming
Hong, Zhouzhenyan
author_sort Jin, Hairong
collection PubMed
description The wide usage of 3D mesh models greatly increases the importance of an effective matching algorithm for them. In this paper, we propose a novel 3D model matching algorithm. Firstly, vertices on the input 3D mesh models are mapped to 1D space by employing Isomap. A pose-invariant feature set is then constructed from the vertices in 1D space. Finally, the similarity between any two 3D models can be computed by comparing their feature sets. Experimental results show that the algorithm is not only invariant to translation, rotation, scaling, but also invariant to different poses of 3D models. Additionally, the algorithm is robust to noise.
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spelling pubmed-89262032022-03-17 Pose-invariant matching for non-rigid 3D models using Isomap Jin, Hairong Huang, Haichao Wang, Zhiqiang Xie, Yuqing Zhou, Xinyue Huang, Liming Hong, Zhouzhenyan PLoS One Research Article The wide usage of 3D mesh models greatly increases the importance of an effective matching algorithm for them. In this paper, we propose a novel 3D model matching algorithm. Firstly, vertices on the input 3D mesh models are mapped to 1D space by employing Isomap. A pose-invariant feature set is then constructed from the vertices in 1D space. Finally, the similarity between any two 3D models can be computed by comparing their feature sets. Experimental results show that the algorithm is not only invariant to translation, rotation, scaling, but also invariant to different poses of 3D models. Additionally, the algorithm is robust to noise. Public Library of Science 2022-03-16 /pmc/articles/PMC8926203/ /pubmed/35294440 http://dx.doi.org/10.1371/journal.pone.0264192 Text en © 2022 Jin et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Jin, Hairong
Huang, Haichao
Wang, Zhiqiang
Xie, Yuqing
Zhou, Xinyue
Huang, Liming
Hong, Zhouzhenyan
Pose-invariant matching for non-rigid 3D models using Isomap
title Pose-invariant matching for non-rigid 3D models using Isomap
title_full Pose-invariant matching for non-rigid 3D models using Isomap
title_fullStr Pose-invariant matching for non-rigid 3D models using Isomap
title_full_unstemmed Pose-invariant matching for non-rigid 3D models using Isomap
title_short Pose-invariant matching for non-rigid 3D models using Isomap
title_sort pose-invariant matching for non-rigid 3d models using isomap
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8926203/
https://www.ncbi.nlm.nih.gov/pubmed/35294440
http://dx.doi.org/10.1371/journal.pone.0264192
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