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Article

A Deep Learning Method for 3D Object Classification and Retrieval Using the Global Point Signature Plus and Deep Wide Residual Network

1
Department of Artificial Intelligence Convergence, Pukyong National University, Busan 48513, Korea
2
Department of Computer Engineering, Dong-A University, Busan 49315, Korea
3
Department of IT Convergence and Application Engineering, Pukyong National University, Busan 48513, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Chang Choi
Sensors 2021, 21(8), 2644; https://doi.org/10.3390/s21082644
Received: 14 March 2021 / Revised: 4 April 2021 / Accepted: 5 April 2021 / Published: 9 April 2021
(This article belongs to the Special Issue Computational Intelligence and Intelligent Contents (CIIC))
A vital and challenging task in computer vision is 3D Object Classification and Retrieval, with many practical applications such as an intelligent robot, autonomous driving, multimedia contents processing and retrieval, and augmented/mixed reality. Various deep learning methods were introduced for solving classification and retrieval problems of 3D objects. Almost all view-based methods use many views to handle spatial loss, although they perform the best among current techniques such as View-based, Voxelization, and Point Cloud methods. Many views make network structure more complicated due to the parallel Convolutional Neural Network (CNN). We propose a novel method that combines a Global Point Signature Plus with a Deep Wide Residual Network, namely GPSP-DWRN, in this paper. Global Point Signature Plus (GPSPlus) is a novel descriptor because it can capture more shape information of the 3D object for a single view. First, an original 3D model was converted into a colored one by applying GPSPlus. Then, a 32 × 32 × 3 matrix stored the obtained 2D projection of this color 3D model. This matrix was the input data of a Deep Residual Network, which used a single CNN structure. We evaluated the GPSP-DWRN for a retrieval task using the Shapnetcore55 dataset, while using two well-known datasets—ModelNet10 and ModelNet40 for a classification task. Based on our experimental results, our framework performed better than the state-of-the-art methods. View Full-Text
Keywords: Global Point Signature Plus; Deep Wide Residual Network; 3D object classification and retrieval; multimedia contents processing and retrieval Global Point Signature Plus; Deep Wide Residual Network; 3D object classification and retrieval; multimedia contents processing and retrieval
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MDPI and ACS Style

Hoang, L.; Lee, S.-H.; Kwon, K.-R. A Deep Learning Method for 3D Object Classification and Retrieval Using the Global Point Signature Plus and Deep Wide Residual Network. Sensors 2021, 21, 2644. https://doi.org/10.3390/s21082644

AMA Style

Hoang L, Lee S-H, Kwon K-R. A Deep Learning Method for 3D Object Classification and Retrieval Using the Global Point Signature Plus and Deep Wide Residual Network. Sensors. 2021; 21(8):2644. https://doi.org/10.3390/s21082644

Chicago/Turabian Style

Hoang, Long, Suk-Hwan Lee, and Ki-Ryong Kwon. 2021. "A Deep Learning Method for 3D Object Classification and Retrieval Using the Global Point Signature Plus and Deep Wide Residual Network" Sensors 21, no. 8: 2644. https://doi.org/10.3390/s21082644

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