# A Voxel Generator Based on Autoencoder
## 4 Performance Evaluation
### Comparison with Related Approaches  
In this experiment, we compared the proposed voxel generator with related approaches including 3D-R2N2, V3 DOR-AE, Pix2 Vox, and TMVNet in terms of IOU. The experimental results are shown in Table 5. For the car and chair class, TMVNet could \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Encoder** & **Decoder** & **Car** & **Chair** & **Lamp** & **Table** \\ \hline \multirow{3}{*}{MVGG16} & Deconv & 0.857 & 0.601 & 0.55 & 0.676 \\  & MSConv & 0.861 & 0.63 & 0.571 & 0.697 \\  & MMConv & 0.852 & 0.587 & 0.539 & 0.671 \\ \hline \multirow{3}{*}{MResNet18} & Deconv & 0.845 & 0.624 & 0.542 & 0.68 \\  & MSConv & 0.858 & 0.65 & 0.564 & 0.711 \\ \cline{1-1}  & MMConv & 0.849 & 0.656 & 0.567 & 0.715 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: IOU values of VoxGen under different encoder-decoder combinations. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Encoder** & **Decoder** & **Flatten Type** & **Parameter No.** & **Model Size** \\ \hline \multirow{3}{*}{MVGG16} & MSConv & GAP & 59,880,232 & 701,943 (KB) \\  & FF & 122,793,921 & 1,439,246 (KB) \\ \cline{2-5}  & MMConv & GAP & 60,307,457 & 707,081 (KB) \\  & FF & 123,181,201 & 1,443,880 (KB) \\ \hline \multirow{3}{*}{MResNet18} & MSConv & GAP & 57,726,721 & 676,896 (KB) \\  & FF & 120,641,281 & 1,414,176 (KB) \\ \cline{1-1} \cline{2-5}  & MMConv & GAP & 58,115,073 & 684,524 (KB) \\ \cline{1-1}  & FF & 121,029,633 & 1,418,804 (KB) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Parameter numbers and model size of VoxGen with FF or GAP. generate the highest IOU value, i.e., 0.870 and 0.721. By contrast, VoxGen yielded the best IOU values, i.e., 0.597, and 0.716, for the lamp and table classes, respectively, when it used the MResNet18 encoder, the FF layer, and the MMConv decoder. It seems that these two methods have different strengths and weaknesses with different object classes. VoxGen has a deeper and wider encoder network and a loss function, which can prevent the large-number of samples from dominating the output voxel. In contrast, the strength of TMVNet is its efficacy at aggregating the position information from multiple-view images. On the other hand, VoxGen outperformed 3D-R2N2 for the four object classes, regardless of which combination of encoder, decoder, and flattened layer was used. The main reason for this is that 3D-R2N2 uses LSTM, which is sensitive to the sequence of input images, as well as the up-pooling that always adds zero in the empty entries in the generated voxel models. By contrast, VoxGen adopts CNN, which is independent of the sequence of input images and the deconvolution layer that can properly estimate the values added to the empty entries in the generated voxel models. The same situation also occurred in the comparison between VoxGen and V3 DOR-AE. VoxGen adopts a deeper encoder network for extracting image features and a wider fully connected network to increase learning ability than V3 DOR-AE. In addition, it adds the convolution layer behind the deconvolution layer in the decoder to enhance the properties of the generated voxels, while V3 DOR-AE only uses deconvolution. These differences allowed VoxGen to outperform V3 DOR-AE. Figure 8 is a visual comparison of voxels generated by our approach and Pix2 Vox. As shown in Table 5, our approach was worse than Pix2 Vox only for the car class in some