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Article

DGCB-Net: Dynamic Graph Convolutional Broad Network for 3D Object Recognition in Point Cloud

1
School of Information Science and Technology, North China University of Technology, Beijing 100144, China
2
Department of Computer and Information Science, University of Macau, Macau 999078, China
3
Department of Multimedia Engineering, Dongguk University-Seoul, Seoul 04620, Korea
*
Author to whom correspondence should be addressed.
Received: 19 October 2020 / Revised: 18 December 2020 / Accepted: 21 December 2020 / Published: 26 December 2020
3D (3-Dimensional) object recognition is a hot research topic that benefits environment perception, disease diagnosis, and the mobile robot industry. Point clouds collected by range sensors are a popular data structure to represent a 3D object model. This paper proposed a 3D object recognition method named Dynamic Graph Convolutional Broad Network (DGCB-Net) to realize feature extraction and 3D object recognition from the point cloud. DGCB-Net adopts edge convolutional layers constructed by weight-shared multiple-layer perceptrons (MLPs) to extract local features from the point cloud graph structure automatically. Features obtained from all edge convolutional layers are concatenated together to form a feature aggregation. Unlike stacking many layers in-depth, our DGCB-Net employs a broad architecture to extend point cloud feature aggregation flatly. The broad architecture is structured utilizing a flat combining architecture with multiple feature layers and enhancement layers. Both feature layers and enhancement layers concatenate together to further enrich the features’ information of the point cloud. All features work on the object recognition results thus that our DGCB-Net show better recognition performance than other 3D object recognition algorithms on ModelNet10/40 and our scanning point cloud dataset. View Full-Text
Keywords: point cloud analysis; 3D object recognition; broad learning system; dynamic graph convolution point cloud analysis; 3D object recognition; broad learning system; dynamic graph convolution
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MDPI and ACS Style

Tian, Y.; Chen, L.; Song, W.; Sung, Y.; Woo, S. DGCB-Net: Dynamic Graph Convolutional Broad Network for 3D Object Recognition in Point Cloud. Remote Sens. 2021, 13, 66. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13010066

AMA Style

Tian Y, Chen L, Song W, Sung Y, Woo S. DGCB-Net: Dynamic Graph Convolutional Broad Network for 3D Object Recognition in Point Cloud. Remote Sensing. 2021; 13(1):66. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13010066

Chicago/Turabian Style

Tian, Yifei, Long Chen, Wei Song, Yunsick Sung, and Sangchul Woo. 2021. "DGCB-Net: Dynamic Graph Convolutional Broad Network for 3D Object Recognition in Point Cloud" Remote Sensing 13, no. 1: 66. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13010066

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