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3D Shape Classification Using a Single View
IEEE access, 2020, Vol.8, p.200812-200822
[Peer Reviewed Journal]
Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2020 ;ISSN: 2169-3536 ;EISSN: 2169-3536 ;DOI: 10.1109/ACCESS.2020.3035583 ;CODEN: IAECCG
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Title:
3D Shape Classification Using a Single View
Author:
Ding, Bo
;
Tang, Lei
;
Gao, Zheng
;
He, Yongjun
Subjects:
3D shape classifier
;
Artificial neural networks
;
Classification
;
Classifiers
;
Complexity theory
;
convolutional neural network
;
Feature extraction
;
Image recognition
;
Information retrieval
;
Machine vision
;
Model accuracy
;
Object recognition
;
representative view
;
Shape
;
Shape classification
;
Shape recognition
;
Solid modeling
;
Three dimensional models
;
Three-dimensional displays
;
Training
Is Part Of:
IEEE access, 2020, Vol.8, p.200812-200822
Description:
View-based 3D shape classification is widely used in machine vision, information retrieval and other fields. However, there are two problems in current methods. First, current 3D shape classifiers fail to make good use of pose information of 3D shapes. Secondly, many views are required to obtain good classification accuracy, which leads to low efficiency. In order to solve these problems, we propose a novel 3D shape classification method based on Convolutional Neural Network (CNN). In the training stage, this method first learns a CNN to extract features, and then uses features of views from different viewpoint groups to train six 3D shape classifiers which fully mine the pose information of 3D shapes. Meanwhile, an additional class is adopted to improve the discrimination of 3D shape classifiers. In the recognition stage, the weighted fusion of image clarity evaluation functions is used to select the most representative view for the 3D shape recognition. Experiments on the ModelNet10 and ModelNet40 show that the classification accuracy of the proposed method can reach up to 91.18% and 89.01% when only using a single view and the efficiency is improved substantially.
Publisher:
Piscataway: IEEE
Language:
English
Identifier:
ISSN: 2169-3536
EISSN: 2169-3536
DOI: 10.1109/ACCESS.2020.3035583
CODEN: IAECCG
Source:
IEEE Xplore Open Access Journals
DOAJ Directory of Open Access Journals
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