# Improved Patch Packing and Refining Segmentation for the V-PCC Standard
## 3 Proposed Method
### Data-Adaptive Patch Packing (DAPP)  
Considering that the occupied graph area is too large and more small patches will increase the burden on the original packaging algorithm. This paper proposes Data-adaptive Patch Packing (DAPP). The goal is to adaptively find the optimal position for each patch based on its characteristics to decrease the size of the occupancy map. The DAPP mainly includes setting up metrics, patch sorting, and patch positioning. Patch sorting refers to the process of sorting the newly generated patches based on a specific metric. Subsequent patch positions will then be performed in this sorted order. The original patch packing method first sorts the patches according to their height values and then positions them on a 2D grid. Through experiments, it has been found that many patches have the same height, as shown in Table 3. In such cases, additional information needs to be taken into consideration for sorting. To make the sorting more effective. The occupancy map generated by the patch packaging does not include attribute information. The sorting metrics should primarily be associated with the geometric features of the patch. The size of the occupancy map will affect the final compression performance. Referring to [34], the evaluation metrics used in this paper are height (H), Patch Bounding Box Size (\(S_{B}\)), and Patch Valid Size (\(S_{V}\)). \(S_{V}\) represents the number of pixels occupied in the 2D patch bounding box. The height is the original ordering metric of V-PCC. The DAPP first sorts patches by height. \(S_{B}\) and \(S_{V}\) should also be considered for patches with the same height value. A new formula has been designed for this purpose, as follows:  
\[S=\eta\cdot S_{B}+\mu\cdot S_{V}\;(\eta+\mu=1), \tag{8}\]  
where \(\eta\) is equal to 0.4 and \(\mu\) is equal to 0.6. The patches with a larger \(S\) value are preferentially positioned. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Test Point Cloud** & **Patches Count** & \begin{tabular}{c} **Number of Patches with the Same Height** \\ \end{tabular} \\ \hline \hline \begin{tabular}{c} Loot \\ Redandblack \\ Soldier \\ Longdress \\ \end{tabular} & \
\begin{tabular}{c} 34 \\ 115 \\ 120 \\ 51 \\ 51 \\ 68 \\ 91 \\ \end{tabular} &
\begin{tabular}{c} 24 \\ 105 \\ 111 \\ 43 \\ 60 \\ 80 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Number of patches with the same height in a point cloud video frame. After patch sorting, we will use the positioning metrics to determine where the patch should be positioned. The positioning metrics are a standard used to measure the position of patches. Each patch must calculate the corresponding positioning indicator to determine its final position. The calculation of positioning metrics comprehensively considers the characteristics of patches that have been packaged and to be packaged. Specifically, this includes the Common Area Length, recorded as \(L_{C}\), and the height of the generated bounding box, recorded as \(H_{B}\). It is shown in Figure 8. \(L_{C}\) is the alignment between the two patches and is equivalent to the length of the common portion of the two patches projected onto the \(Y\)-axis. These positioning metrics need to be calculated during the packing process. The patch to be positioned is compared with each previously positioned patch according to the positioning metrics in order to select the best position. Once the packaging is completed, an occupancy map can be created to store these patches. Currently, N patches have been placed, and the patch selected for reference is labeled as P. Its width is \(w\) and height is \(h\). The patch to be placed is the (N + 1)th patch, labeled as P\({}^{\prime}\). Its width is \(w^{\prime}\) and height is \(h^{\prime}\). The feature point used is the point at the upper left corner of the bounding box of P\({}^{\prime}\).