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.
Highlights
In recent years, point clouds have been researched in various fields, such as autonomous robot systems [1], three-dimensional (3D) face recognition [2], intelligence surveillance [3], and 3D modeling [4]
This paper proposes a 3D object classification system using broad learning system (BLS) with a pretrained feature extractor for point cloud data
VoxNet [26] was the most affected by Gaussian noise, and its accuracy dropped by half on
Summary
Point clouds have been researched in various fields, such as autonomous robot systems [1], three-dimensional (3D) face recognition [2], intelligence surveillance [3], and 3D modeling [4]. When using most existing neural network structures, point clouds are hard to process directly because of their irregularity. To solve this problem, researchers developed various methods to extract features from raw point clouds for existing deep learning structures, such as spin image (SI) [5], clustered viewpoint feature histogram [6], and view feature histogram [7]. As deep learning is gaining success in many fields, researchers proposed or employed several deep learning structures for different tasks [8,9,10], e.g., 3D model retrieval. It is inconvenient to add new categories into a trained deep learning network or change the structure of a deep learning network, because the
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