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International Journal of Multimedia Information Retrieval

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21-04-2022 | Regular Paper

DC-GNN: drop channel graph neural network for object classification and part segmentation in the point cloud

Authors: Md Meraz, Md Afzal Ansari, Mohammed Javed, Pavan Chakraborty

Published in: International Journal of Multimedia Information Retrieval | Issue 2/2022

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Abstract

In the recent years, the problem of 3D shape analysis in the point cloud is considered as one of the challenging research topics in the field of computer vision. The major issues here are effective representation of the 3D information, meaningful feature extraction and subsequent task of classification. In this research paper, a deep learning-based network called Drop Channel Graph Neural Network (DC-GNN) is proposed for object classification and part segmentation. The DC-GNN model employs the idea of k-NN-based drop channel with hierarchical feature selection approach at each layer for dynamic graph construction, and further, with the help of Multi-Layer Perceptron Networks accomplishes the task of object classification. The same DC-GNN model is extended to carry out part segmentation in the point cloud data using the ShapeNet-Part benchmark dataset. The proposed network reports the state-of-the-art classification accuracy of 93.64% with ModelNet-40 dataset (Source-Code-https://github.com/merazlab/DC-GNN).

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Title: DC-GNN: drop channel graph neural network for object classification and part segmentation in the point cloud
Authors: Md Meraz
Md Afzal Ansari
Mohammed Javed
Pavan Chakraborty
Publication date: 21-04-2022
Publisher: Springer London
Published in: International Journal of Multimedia Information Retrieval / Issue 2/2022
Print ISSN: 2192-6611
Electronic ISSN: 2192-662X
DOI: https://doi.org/10.1007/s13735-022-00236-7

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