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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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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. |
format | Online Article Text |
id | pubmed-8926203 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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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