# VB-Net: Voxel-Based Broad Learning Network for 3D Object Classification
## 4 Experiments and Analysis
### Performance of VB-Net  
The performance of the BLS is affected by the number of enhancement nodes and the number of feature nodes [11]. Therefore, we designed two sets of experiments to test our system. First, we set 900 feature nodes in our system, increasing the enhancement nodes. We added \(26\times 26\times 26\) and \(38\times 38\times 38\) voxel resolutions to observe the performance of VB-Net under different resolutions. Experimental results on the ModelNet40 dataset are shown in Table 1.  
As shown in Table 1, the average accuracy initially increased with the number of enhancement nodes. The accuracy reached its maximum with 2500 enhancement nodes in VB-Net under \(20\times 20\times 20\) resolution (82.61%) and \(38\times 38\times 38\) resolution (80.75%). Under \(26\times 26\times 26\) resolution or \(32\times 32\times 32\) resolution, the accuracy reached its max value with 1500 nodes in the enhancement layer (80.75%) or 3000 enhancement nodes in the system (82.41%). A further increase in the number of enhancement nodes no longer improved accuracy; on the contrary, the classification accuracy then decreased with the number of enhancement nodes. Another set of experiments involved increasing feature nodes in VB-Net. According to Table 1, VB-Net showed good performance with 3000 enhancement nodes in the enhancement layer at both resolutions. Hence, we set the number of enhancement nodes to 3000 and kept increasing the feature nodes in VB-Net. The results of these experiments on the ModelNet40 dataset are shown in Table 2.  
The highest accuracy reached 82.41% when 800 feature nodes were used in the proposed network under \(20\times 20\times 20\) resolution or 80.59% with 400 feature nodes under \(26\times 26\times 26\) resolution. When 100 feature nodes were used in VB-Net, the highest accuracy reached 83.99% under \(32\times 32\times 32\) resolution. For \(38\times 38\times 38\) resolution, the highest accuracy was 80.99% with 500 feature nodes in the system. Unlike the first set of experiments, classification accuracy under \(32\times 32\times 32\) resolution decreased directly. From the data in Tables 1 and 2, it is evident that the performance of VB-Net improved as more feature nodes or enhancement nodes were utilized in the calculation of final results. When the number of nodes exceeded the peak amount at which performance was best, the performance of VB-Net became worse due to the interference of too much data. The appropriate number varied with voxel resolutions and strategies. The time consumption in training and test processes for two different training strategies mentioned above under \(20\times 20\times 20\) voxel resolution was also computed. Although the time consumption increased  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Enhancement Nodes**} & **Average Accuracy** & **Average Accuracy** & **Average Accuracy** & **Average Accuracy** \\  & **(20\(\times\) 20\(\times\) 20** & **(26\(\times\) 26**\(\times\) 26** & **(32\(\times\) 32** & **(38\times 38\times 38\)** \\  & **Voxel)** & **Voxel)** & **Voxel)** \\ \hline
100 & 72.20\% & 72.28\% & 73.86\% & 73.37\% \\
200 & 72.44\% & 72.48\% & 74.43\% & 73.50\% \\
300 & 72.77\% & 72.97\% & 74.87\% & 73.86\% \\
400 & 72.89\% & 72.85\% & 74.91  
with the number of nodes in the network, time consumption with increasing feature nodes increased more quickly than with increasing enhancement nodes, as shown in Figure 4a,b. However, the former strategy held fewer nodes in the network, which was different from the case where time consumption increased with the number of nodes in the network. We tested the situation with 5900 nodes in network (900 feature nodes and 5000 enhancement nodes), where the time consumption was 38.86 s in total, which was far shorter than the method with increasing feature nodes.