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

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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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Acknowledgements

The author gratefully acknowledges the support of Central Computing Facility (CCF) of Indian Institute of Information Technology Allahabad, and the resources provided by PARAM Shivay Facility under the National Supercomputer Mission, at the IIT-BHU Varanasi. This research work is carried out with the support received from the Ministry of Education, Government of India.

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Authors and Affiliations

  1. Department of IT, Center of Intelligent Robotics (CIR Lab), Indian Institute of Information Technology Allahabad, Prayagraj, U.P, 211012, India

    Md Meraz, Md Afzal Ansari, Mohammed Javed & Pavan Chakraborty

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  1. Md Meraz
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  2. Md Afzal Ansari
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  3. Mohammed Javed
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  4. Pavan Chakraborty
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Correspondence to Md Meraz.

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Meraz, M., Ansari, M.A., Javed, M. et al. DC-GNN: drop channel graph neural network for object classification and part segmentation in the point cloud. Int J Multimed Info Retr 11, 123–133 (2022). https://doi.org/10.1007/s13735-022-00236-7

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