# VB-Net: Voxel-Based Broad Learning Network for 3D Object Classification
## 4 Experiments and Analysis
### Performance of Feature Extractor  
To evaluate the effect of our feature extractor, we employed an ablation experiment on the ModelNet10 dataset, as shown in Table 4, with \(32\times 32\times 32\) voxel resolution. We set 450 feature nodes and 1100 enhancement nodes in BLS and VB-Net. The feature extractor was trained 30 times. FE refers to the VoxNet feature extractor and OFE refers to the original VoxNet [26]. The \"time\" column in Table 4 records the total time consumption (training time pluses test time) of one module.  
Comparing models A, C, and D, the feature extractor had great influence on total time consumption. BLS with a feature extractor ran much faster than the original BLS, and the classification accuracy also improved by about 3%. According to average accuracy and total time consumption, BLS performed better than the deep learning network. Although model E reached higher classification accuracy than model B, model C still achieved higher accuracy than model D. This proves that our feature extractor is more efficient than VoxNet [26] when combined with BLS.  
We adopted another ablation experiment to examine whether our feature extractor could be more efficient. Experiments were done on the ModelNet10 dataset under \(20\times 20\times 20\) voxel resolution using feature extractors trained 120 times. Settings of VB-Net were 420 feature nodes and 900 enhancement nodes. Results are presented in Table 5.  
Model F gained higher accuracy than our method (model I) because more features were input into BLS in model F, but our method ran faster than F. When comparing models F, G, and H, the max pooling layer had an influence on the performance of the feature extractor. Without the max pooling layer, the classification accuracy of VB-Net dropped about 2%. Performance on models G and F proved that the first 3D convolutional layer not only reduced the input features of BLS, but also improved the accuracy. The second convolutional layer helped VB-Net run more efficiently by reducing the input features of BLS.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **FE** & **BLS** & **OFE** & **Average Accuracy** & **Time (s)** \\ \hline A & & \(\surd\) & & 86.78\% & 129.36 \\ B & \(\surd\) & & & 85.35\% & 3111.12 \\ C & \(\surd\) & \(\surd\) & & 89.86\% & 39.13 \\ D & & \(\surd\) & \(\surd\) & 89.75\% & 38.40 \\ E & & & \(\surd\) & 86.12\% & 3573.78 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation experiment of VB-Net. FE, feature extractor; OFE, original feature extractor (VoxNet).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Input** & \
\begin{tabular}{c} **Average Accuracy** \\ **(ModelNet10)** \\ \end{tabular} &
\begin{tabular}{c} **Average Accuracy** \\ **(ModelNet10)** \\ \end{tabular} \\ \hline Our method (\(20\times 20\times 20\) voxel) & Voxel & 82.41\% & 89.64\% \\ Our method (\(32\times 32\times 32\) voxel) & Voxel & 83.99\% & 90.08\% \\
3 DShapeNets [27] & Voxel & 77\% & 83.5\% \\ VoxNet [26] & Voxel & 83\% & 92\% \\ DeepPano [28] & Image & 77.63\% & 85.45\% \\ Geometry Image [29] & Image & 83.9\% & 88.4\% \\ [PERSON] et al. [30] & Image & 82.10\% & - \\ ECC [31] & Graph & 83.2\% & 90.0\% \\ PointNet [32] & Point & - & 77.6\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison with existing deep learning networks.