VB-Net: Voxel-based broad learning network for 3D object classification

Zishu Liu, Wei Song, Yifei Tian, Sumi Ji, Yunsick Sung, Long Wen, Tao Zhang, Liangliang Song, Amanda Gozho

Research output: Contribution to journalArticlepeer-review

23 Scopus citations

Abstract

Point clouds have been widely used in three-dimensional (3D) object classification tasks, i.e., people recognition in unmanned ground vehicles. However, the irregular data format of point clouds and the large number of parameters in deep learning networks affect the performance of object classification. This paper develops a 3D object classification system using a broad learning system (BLS) with a feature extractor called VB-Net. First, raw point clouds are voxelized into voxels. Through this step, irregular point clouds are converted into regular voxels which are easily processed by the feature extractor. Then, a pre-trained VoxNet is employed as a feature extractor to extract features from voxels. Finally, those features are used for object classification by the applied BLS. The proposed system is tested on the ModelNet40 dataset and ModelNet10 dataset. The average recognition accuracy was 83.99% and 90.08%, respectively. Compared to deep learning networks, the time consumption of the proposed system is significantly decreased.

Original languageEnglish
Article number6735
Pages (from-to)1-13
Number of pages13
JournalApplied Sciences (Switzerland)
Volume10
Issue number19
DOIs
StatePublished - 1 Oct 2020

Keywords

  • 3D object classification
  • Broad learning system
  • Point cloud

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