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Robust Mesh Segmentation Using Feature-Aware Region Fusion †
This paper introduces a simple but powerful segmentation algorithm for 3D meshes. Our algorithm consists of two stages: over-segmentation and region fusion. In the first stage, adaptive space partition is applied to perform over-segmentation, which is very efficient. In the second stage, we define a...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9824490/ https://www.ncbi.nlm.nih.gov/pubmed/36617011 http://dx.doi.org/10.3390/s23010416 |
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author | Wu, Lulu Hou, Yu Xu, Junli Zhao, Yong |
author_facet | Wu, Lulu Hou, Yu Xu, Junli Zhao, Yong |
author_sort | Wu, Lulu |
collection | PubMed |
description | This paper introduces a simple but powerful segmentation algorithm for 3D meshes. Our algorithm consists of two stages: over-segmentation and region fusion. In the first stage, adaptive space partition is applied to perform over-segmentation, which is very efficient. In the second stage, we define a new intra-region difference, inter-region difference, and fusion condition with the help of various shape features and propose an iterative region fusion method. As the region fusion process is feature aware, our algorithm can deal with complex 3D meshes robustly. Massive qualitative and quantitative experiments also validate the advantages of the proposed algorithm. |
format | Online Article Text |
id | pubmed-9824490 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-98244902023-01-08 Robust Mesh Segmentation Using Feature-Aware Region Fusion † Wu, Lulu Hou, Yu Xu, Junli Zhao, Yong Sensors (Basel) Article This paper introduces a simple but powerful segmentation algorithm for 3D meshes. Our algorithm consists of two stages: over-segmentation and region fusion. In the first stage, adaptive space partition is applied to perform over-segmentation, which is very efficient. In the second stage, we define a new intra-region difference, inter-region difference, and fusion condition with the help of various shape features and propose an iterative region fusion method. As the region fusion process is feature aware, our algorithm can deal with complex 3D meshes robustly. Massive qualitative and quantitative experiments also validate the advantages of the proposed algorithm. MDPI 2022-12-30 /pmc/articles/PMC9824490/ /pubmed/36617011 http://dx.doi.org/10.3390/s23010416 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Wu, Lulu Hou, Yu Xu, Junli Zhao, Yong Robust Mesh Segmentation Using Feature-Aware Region Fusion † |
title | Robust Mesh Segmentation Using Feature-Aware Region Fusion † |
title_full | Robust Mesh Segmentation Using Feature-Aware Region Fusion † |
title_fullStr | Robust Mesh Segmentation Using Feature-Aware Region Fusion † |
title_full_unstemmed | Robust Mesh Segmentation Using Feature-Aware Region Fusion † |
title_short | Robust Mesh Segmentation Using Feature-Aware Region Fusion † |
title_sort | robust mesh segmentation using feature-aware region fusion † |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9824490/ https://www.ncbi.nlm.nih.gov/pubmed/36617011 http://dx.doi.org/10.3390/s23010416 |
work_keys_str_mv | AT wululu robustmeshsegmentationusingfeatureawareregionfusion AT houyu robustmeshsegmentationusingfeatureawareregionfusion AT xujunli robustmeshsegmentationusingfeatureawareregionfusion AT zhaoyong robustmeshsegmentationusingfeatureawareregionfusion |