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VB-Net: Voxel-Based Broad Learning Network for 3D Object Classification
| Content Provider | MDPI |
|---|---|
| Author | Liu, Zishu Song, Wei Tian, Yifei Ji, Sumi Sung, Yunsick Wen, Long Zhang, Tao Song, Liangliang Gozho, Amanda |
| Copyright Year | 2020 |
| Description | 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. |
| Starting Page | 6735 |
| e-ISSN | 20763417 |
| DOI | 10.3390/app10196735 |
| Journal | Applied Sciences |
| Issue Number | 19 |
| Volume Number | 10 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2020-09-26 |
| Access Restriction | Open |
| Subject Keyword | Applied Sciences Imaging Science Broad Learning System Point Cloud 3d Object Classification |
| Content Type | Text |
| Resource Type | Article |
