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3D model retrieval based on interactive attention CNN and multiple features

3D (three-dimensional) models are widely applied in our daily life, such as mechanical manufacture, games, biochemistry, art, virtual reality, and etc. With the exponential growth of 3D models on web and in model library, there is an increasing need to retrieve the desired model accurately according...

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Detalles Bibliográficos
Autores principales: Gao, Xue-Yao, Jia, Wen-Hui, Zhang, Chun-Xiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10280475/
https://www.ncbi.nlm.nih.gov/pubmed/37346676
http://dx.doi.org/10.7717/peerj-cs.1227
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author Gao, Xue-Yao
Jia, Wen-Hui
Zhang, Chun-Xiang
author_facet Gao, Xue-Yao
Jia, Wen-Hui
Zhang, Chun-Xiang
author_sort Gao, Xue-Yao
collection PubMed
description 3D (three-dimensional) models are widely applied in our daily life, such as mechanical manufacture, games, biochemistry, art, virtual reality, and etc. With the exponential growth of 3D models on web and in model library, there is an increasing need to retrieve the desired model accurately according to freehand sketch. Researchers are focusing on applying machine learning technology to 3D model retrieval. In this article, we combine semantic feature, shape distribution features and gist feature to retrieve 3D model based on interactive attention convolutional neural networks (CNN). The purpose is to improve the accuracy of 3D model retrieval. Firstly, 2D (two-dimensional) views are extracted from 3D model at six different angles and converted into line drawings. Secondly, interactive attention module is embedded into CNN to extract semantic features, which adds data interaction between two CNN layers. Interactive attention CNN extracts effective features from 2D views. Gist algorithm and 2D shape distribution (SD) algorithm are used to extract global features. Thirdly, Euclidean distance is adopted to calculate the similarity of semantic feature, the similarity of gist feature and the similarity of shape distribution feature between sketch and 2D view. Then, the weighted sum of three similarities is used to compute the similarity between sketch and 2D view for retrieving 3D model. It solves the problem that low accuracy of 3D model retrieval is caused by the poor extraction of semantic features. Nearest neighbor (NN), first tier (FT), second tier (ST), F-measure (E(F)), and discounted cumulated gain (DCG) are used to evaluate the performance of 3D model retrieval. Experiments are conducted on ModelNet40 and results show that the proposed method is better than others. The proposed method is feasible in 3D model retrieval.
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spelling pubmed-102804752023-06-21 3D model retrieval based on interactive attention CNN and multiple features Gao, Xue-Yao Jia, Wen-Hui Zhang, Chun-Xiang PeerJ Comput Sci Computer Aided Design 3D (three-dimensional) models are widely applied in our daily life, such as mechanical manufacture, games, biochemistry, art, virtual reality, and etc. With the exponential growth of 3D models on web and in model library, there is an increasing need to retrieve the desired model accurately according to freehand sketch. Researchers are focusing on applying machine learning technology to 3D model retrieval. In this article, we combine semantic feature, shape distribution features and gist feature to retrieve 3D model based on interactive attention convolutional neural networks (CNN). The purpose is to improve the accuracy of 3D model retrieval. Firstly, 2D (two-dimensional) views are extracted from 3D model at six different angles and converted into line drawings. Secondly, interactive attention module is embedded into CNN to extract semantic features, which adds data interaction between two CNN layers. Interactive attention CNN extracts effective features from 2D views. Gist algorithm and 2D shape distribution (SD) algorithm are used to extract global features. Thirdly, Euclidean distance is adopted to calculate the similarity of semantic feature, the similarity of gist feature and the similarity of shape distribution feature between sketch and 2D view. Then, the weighted sum of three similarities is used to compute the similarity between sketch and 2D view for retrieving 3D model. It solves the problem that low accuracy of 3D model retrieval is caused by the poor extraction of semantic features. Nearest neighbor (NN), first tier (FT), second tier (ST), F-measure (E(F)), and discounted cumulated gain (DCG) are used to evaluate the performance of 3D model retrieval. Experiments are conducted on ModelNet40 and results show that the proposed method is better than others. The proposed method is feasible in 3D model retrieval. PeerJ Inc. 2023-02-10 /pmc/articles/PMC10280475/ /pubmed/37346676 http://dx.doi.org/10.7717/peerj-cs.1227 Text en © 2023 Gao 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, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.
spellingShingle Computer Aided Design
Gao, Xue-Yao
Jia, Wen-Hui
Zhang, Chun-Xiang
3D model retrieval based on interactive attention CNN and multiple features
title 3D model retrieval based on interactive attention CNN and multiple features
title_full 3D model retrieval based on interactive attention CNN and multiple features
title_fullStr 3D model retrieval based on interactive attention CNN and multiple features
title_full_unstemmed 3D model retrieval based on interactive attention CNN and multiple features
title_short 3D model retrieval based on interactive attention CNN and multiple features
title_sort 3d model retrieval based on interactive attention cnn and multiple features
topic Computer Aided Design
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10280475/
https://www.ncbi.nlm.nih.gov/pubmed/37346676
http://dx.doi.org/10.7717/peerj-cs.1227
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