# Cube of Space Sampling for 3D Model Retrieval
## 5 Conclusions  
In this paper, we propose a novel approach for 3D model re-sampling, namely CSS (Cube of Space Sampling). It is a kind of voxelization method and can be transformed into the voxel-based feature for 3D model classification and retrieval. Particularly, the voxel-based feature via the CSS method can represent 3D models well, and it can also solve the 3D model normalization problem (e.g., rotation problem). The proposed CSS method can work on the non-completely closed 3D models without failure. Therefore, the CSS method can be fully applied to all types of 3D models.  
The experimental results based on the ESB dataset show that the proposed method is indeed able to effectively extract useful features for 3D model retrieval. More specifically, the CSS features can improve classification accuracy by SVM when compared with a state-of-the-art technique. However, our proposed approach does not aim to replace the existing features, but to provide new data to extract important features. Therefore, the CSS features provide an opportunity for future research, which could use a different form of data for feature extraction and increase the accuracy of classification. On the other hand, this proposed method still has some shortcomings. For example, the computational cost is high during feature extraction.  
There are several issues to be addressed in future work. In this study, since the CSS data were only based on some simple statistics to create a set of features, other methods of dealing with our CSS data to highlight the characteristics of each model (such as FFT, etc.) could be applied. In addition, other existing features (e.g., Skeleton) could be combined with the CSS features to further increase the classification accuracy and retrieval effectiveness. On the other hand, as the processing time of the proposed approach is longer than the baseline, some more efficient strategies for space sampling need to be proposed with regards to computational cost during feature extraction. Similarly, for the index issue, the 3D spatial index could be considered to speed up the calculation. Moreover, further performance comparisons could be conducted based on some deep-learning techniques, such as convolutional neural networks [30; 31; 32] and other feature representations, such as view-based 3D model feature representation [33; 34]. Finally, it would be interesting to examine the performance of CSS for different types of 3D objects, such as objects composed of multiple parts that are not watertight, and the sensitivity of CSS when applied to arbitrarily rotated models, or swapping the XYZ axes.  
Conceptualization, Z.-Y.C.; methodology, Z.-Y.C., C.-F.T. and W.-C.L.; software, W.-C.L.; validation, Z.-Y.C. and W.-C.L.; formal analysis, Z.-Y.C., C.-F.T. and W.-C.L.; resources, Z.-Y.C.; data curation, Z.-Y.C.; writing--original draft preparation, Z.-Y.C., C.-F.T. and W.-C.L.; writing--review and editing, Z.-Y.C., C.-F.T. and W.-C.L.; supervision, C.-F.T.; project administration, C.-F.T.; funding acquisition, W.-C.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Science and Technology of Taiwan, grant MOST 110-2410-H-182-002 and Chang Gung Memorial Hospital, Linkou, grant BMRPH13 and CMRPG3J0732.  
Not applicable.  
The work was supported in part by the Ministry of Science and Technology of Taiwan under Grant MOST 110-2410-H-182-002 and in part by Chang Gung Memorial Hospital, Linkou, under Grant BMRPH13 and CMRPG3J0732.