Faster dynamic graph CNN: Faster deep learning on 3d point cloud data

Jinseok Hong, Keeyoung Kim, Hongchul Lee

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)

Abstract

Geometric data are commonly expressed using point clouds, with most 3D data collection devices outputting data in this form. Research on processing point cloud data for deep learning is ongoing. However, it has been difficult to apply such data as input to a convolutional neural network (CNN) or recurrent neural network (RNN) because of their unstructured and unordered features. In this study, this problem was resolved by arranging point cloud data in a canonical space through a graph CNN. The proposed graph CNN works dynamically at each layer of the network and learns the global geometric features by capturing the neighbor information of the points. In addition, by using a squeeze-and-excitation module that recalibrates the information for each layer, we achieved a good trade-off between the performance and the computation cost, and a residual-type skip connection network was designed to train the deep models efficiently. Using the proposed model, we achieved a state-of-the-art performance in terms of classification and segmentation on benchmark datasets, namely ModelNet40 and ShapeNet, while being able to train our model 2 to 2.5 times faster than other similar models.

Original languageEnglish
Pages (from-to)190529-190538
Number of pages10
JournalIEEE Access
Volume8
DOIs
Publication statusPublished - 2020

Bibliographical note

Funding Information:
This work was supported in part by the Ministry of Culture, Sports and Tourism (MCST), and in part by the Korea Creative Content Agency (KOCCA), Culture Technology (CT) Research and Development Program, in 2020.

Publisher Copyright:
© 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

Keywords

  • Classification
  • Deep learning
  • Graph CNN
  • Point cloud
  • Segmentation

ASJC Scopus subject areas

  • General Computer Science
  • General Materials Science
  • General Engineering
  • Electrical and Electronic Engineering

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