# Improved Patch Packing and Refining Segmentation for the V-PCC Standard
## 3 Proposed Method  
### V-PCC Method and Its Limitations Explained  
The latest refining segmentation method provided by V-PCC is the grid-based refining segmentation [33]. It simplifies the redundant process of point-based refining segmentation by incorporating grid constraints. The refining segmentation iteratively updates the projection plane index based on the results of the initial segmentation. We are investigating the time consumption ratio of each process in patch generation. The time consumed by refining segmentation is shown in Table 1, up to 59.48%. This is because the process operates indiscriminately on all voxels without considering the differences between them. We proposed the VS-RS. It can effectively utilize the characteristics of different voxels.  
The packing method in V-PCC utilizes an exhaustive search algorithm. It first determines the order in which patches are packed. Then, the raster scans the 2D grid to find the correct position, ensuring that the patches are positioned without overlapping. Grids belonging to a patch are considered occupied blocks. The occupancy maps guide the generation of geometric and attribute videos. The result of packing affects the final RD performance. The patch packing should minimize the size of the map.  
The patch packaging algorithm cannot position the patch very compactly. The occupancy map contains many unoccupied blocks. The black area represents the unoccupied region, as shown in Figure 1(a). It is not helpful for the restoration of point cloud data, but it needs to be compressed. This paper investigates the proportion of unoccupied areas in the occupancy map. Table 2 shows the results. Up to 64.65% of the part is unoccupied area. So, the primary objective is to enhance compression efficiency by increasing the proportion of the occupancy area. In addition, when the VS-RS is added, it generates the occupancy map as shown in Figure 1(b). It can be observed that the VS-RS generates more small patches, which can impose a burden on the original packing algorithm. The other goal of ours is to ensure that small patches find their appropriate place. Therefore, we proposed the DAPP.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test Point Cloud** & \begin{tabular}{c} **Estimate** \\ **Normals** \\ \end{tabular} & \begin{tabular}{c} **Initial** \\ **Segmentation** \\ \end{tabular} & \begin{tabular}{c} **Refining** \\ **Segmentation** \\ \end{tabular} & \
\begin{tabular}{c} **Segment** \\ **Patches** \\ \end{tabular} \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Loot \\ Redandblack \\ Soldier \\ Longdress \\ Basketball\_player \\ \end{tabular} } & 32.71\% & 0.04\% & 55.89\% & 11.36\% \\  & 27.96\% & 0.04\% & 59.48\% & 12.52\% \\  & 28.64\% & 0.03\% & 58.37\% & 12.96\% \\  & 52.65\% & 0.07\% & 29.18\% & 18.11\% \\  & 53.72\% & 0.06\% & 11.79\% & 34.43\% \\ \cline{1-1}  & 53.86\% & 0.06\% & 13.94\% & 32.14\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Time consumption ratio of each procedure in the patch generation process.  
Figure 2: Occupancy map: (**a**) generated by V-PCC; (**b**) generated by adding VS-RS.