# A Voxel Generator Based on Autoencoder
## 5 Conclusions and Future Work  
We have successfully developed a voxel generator called VoxGen, based on an autoencoder. This voxel generator adopts the modified VGG16 and ResNet18 to improve the effectiveness of feature extraction and mixes the deconvolution layer with the convolution layer in the decoder to generate and polish the output voxels. Moreover, it allows users to select the fully flattened or the global average pooling layer to optimize the quality of the generated voxels or reduce the computational cost of model training, respectively.  
We performed a set of experiments to evaluate the impact of different combinations of encoders, decoders, and flattened layers. The conclusions derived from our experiments are as follows. First, a deep feature extraction network is essential for the quality of the generated voxels, especially when the object shape is complex and irregular. However, increasing the depth of the feature extraction network does not always improve the quality of generated voxels, as they are also dependent on the selected flattened layer and the network architecture of the used decoder. Second, adding a convolution layer at the end of consecutive deconvolution layers is useful for improving the quality of the generated voxels. However, adding a convolution layer behind each deconvolution layer does not promise further improvements in the quality of the generated voxels because its effect is dependent on the combined encoder architecture and the object-shape complexity. Third, the fully flattened layer is essential for the quality of the generated voxels, especially when the object shape is complex and irregular. Conversely, the global average pooling layer is useful for increasing the efficiency of the voxel generator, especially when the resolution of the voxels or the architecture complexity of the encoder and decoder is very high. Forth, the best architecture combination of VoxGen for the car class is MVGG16+MSConv+GAP, while that for the chair, lamp, and table classes is MResNet18+MMConv+FF. Consequently, carefully selecting a proper architecture is important for the performance of the voxel generator. Finally, the proposed generator is more effective than approaches such as 3 DR2N2, V3 DOR-AE, or Pix2 Vox because of its better encoder or decoder architecture. However, VoxGen and TMVNet demonstrated different strengths and weaknesses for different object classes.  
Figure 8: Visual comparison of our approach with Pix2 Vox for four test classes.  
Even though we have achieved a significant performance improvement in voxel generation, we will continue to improve the architecture of VoxGen in the future. For example, we will try to decompose objects into a set of components, and then generate the voxel for each component before merging the generated voxel to form a complete voxel model for the object. On the other hand, the voxel resolution of VoxGen is currently only \(32\times 32\). We will try to increase the resolution of the generated voxels while maintaining the efficiency of the voxel generator.  
Conceptualization, B.-C.H. and T.-Y.L.; Data curation, Y.-C.F.; Formal analysis, B.-C.H. and T.-Y.L.; Funding acquisition, T.-Y.L.; Investigation, B.-C.H.; Methodology, B.-C.H.; Project administration, T.-Y.L.; Resources, T.-Y.L.; Software, B.-C.H. and Y.-C.F.; Supervision, T.-Y.L.; Validation, B.-C.H. and T.-Y.L.; Visualization, Y.-C.F.; Writing--original draft, B.-C.H.; Writing--review & editing, T.-Y.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Ministry of Science and Technology in Taiwan, grant number [111-2221-E-992-048].  
We would like to thank the support of the Ministry of Science and Technology in Taiwan on this study under the project number: MOST 111-2221-E-992-048.  
The authors declare no conflict of interest.