# A Novel Preprocessing Method for Dynamic Point-Cloud Compression
## 5 Conclusions  
In this research, we proposed a method to efficiently manage and compress animation information via a 3D point-cloud via experiments using the point conversion method. In particular, a point-cloud preprocessing algorithm for efficiently processing and managing dynamic point-clouds has been proposed. Subtle errors in existing point-clouds are not the right form for duplicate extraction. The proposed method aimed to solve problem by reconfiguring the point-cloud on a voxel basis to eliminate the error. This method repositioned all vertices in the center of the voxel using an expression that converted them to each voxel unit. This result not only eliminated the error, but also removed the unnecessarily high density part from the point-cloud. Checks were undertaken to extract the duplicate vertex of the point-cloud with the error removed in this way. The extracted information was an intersection of both point-clouds. Therefore, this part was shared and used to provide the compression effect. The proposed method showed that the compression  
Figure 8: Data correlation graph according to voxel size change: (**a**) amount of change by voxel size; (**b**) compression result.  
ratio increased from 6.63% to 16.85% in proportion to the voxel size when the file was increased to twice the recording unit in which it was created.  
In an environment where the 3D model to process is not determined by the real-time system, the maximum and minimum performance are not determined. Therefore, we suggest the use the proposed algorithm instead of the larger algorithm to create a reliable service. As a result of finally compressing the file size twice, we expect that the dynamic point-cloud will be able to be managed efficiently. In addition, 3D model analysis can be implemented using patterns of extracted redundant data that are very close to the outline. The results obtained showed minimal damage to rendering quality and maximum compression when voxel size was 1.55.  
The proposed method is expected to be advantageous for compressing point-clouds obtained from different environments and different devices. In particular, as research on acquiring point-clouds on mobile devices with strong device-specific characteristics progresses, it is expected that this method can be applied to easily implement compression. However, more detailed research between compression performance and quality is needed in the future. In this experiment, voxel size was analyzed intensively as a representative. In addition, various voxel sizes need to be studied.  
Conceptualization, M.-y.L. and S.-c.K.; methodology, M.-y.L.; software, M.-y.L.; validation, S.-h.L. ([PERSON]); formal analysis, M.-y.L. and S.-h.L. ([PERSON]); investigation, S.-h.L. ([PERSON]) and K.-d.J.; resources, S.-h.L. ([PERSON]) and S.-h.L. ([PERSON]); data curation, M.-y.L.; writing--original draft preparation, M.-y.L. and S.-c.K.; visualization, M.-y.L. and S.-c.K.; supervision, S.-c.K. and K.-d.J.; project administration, S.-c.K. and K.-d.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy reasons.  
This work was supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (MSIT) (No.2020-0-0192, AR Cloud, Anchor, Augmented Reality, Fog Computing, Mixed Reality).  
The authors declare no conflict of interest.