# TransPGC: Point Cloud Geometry Compression Based on Transformers
## 4 Experiment
### Experiment Results  
The experiment evaluated the compression performance of TransPCGC using five datasets: 8i Voxelized FullBodies (8 iVFB) [62], Owlili dynamic human mesh (Owlii) [63], MUVB [64], ShapeNet-Core [61], and ModelNet40 [65]. Because of the substantial size of the ShapeNet-Core [61] and ModelNet40 [65] datasets, 32 mesh models were randomly selected from each dataset and subjected to random rotations and voxelization. The proposed TransPCGC was compared and analyzed against other point cloud geometry compression algorithms, including G-PCC (O) [20], G-PCC (T) [20], GeoCNNv2 [6], ADL-PCC [14], Learned-PCGC [7], MRM-PCGC [33] and VA-PCC [34]. Among them, MRM-PCGC [33] and VA-PCC [34] utilize data from their papers for comparison. Objective comparisons were performed using BD-rate analysis for lossy compression, measuring point-to-point error (D1), point-to-plane error (D2), and PSNR derived from both metrics. Additionally, the bits per input point (bpp) were used to assess the compression rate. Figure 3 shows the rate-distortion curves for different point clouds. It can be observed that the D1 and D2 metrics of TransPCGC outperform the compared algorithms across different bitrates. The results are shown in Tables 1 and 2. Our method achieved averages \(-80.84\%\) and \(-77.59\%\) gains against G-PCC(T) [20], \(-97.09\%\) and \(-97.37\%\) gains against G-PCC(O) [20], \(-37.9\%\) and \(-40.27\%\) gains against ADL-PCC [14], \(-39.01\%\) and \(-34.64\%\) gains against GeoCNNv2 [6], \(-14.39\%\) and \(-16.66\%\) gains against Learned-PCCGC [7], \(-4.63\%\) and \(-10.78\%\) gains against MRM-PCGC [33], \(-14.12\%\) and \(-15.99\%\) gains against VA-PCGC [34] measured via respective D1 and D2 based BD-Rate. Figure 3: Rate distortion curves plotted on different test samples. From Table 3, it can be observed that our method, TransPCGC, demonstrates superior performance when compared to the G-PCC(O) [20] method on the 8 iVFB, Owili, and MUVB datasets. Across the three datasets, our method achieves an average BD-rate gain of 85.79% and 80.24% for D1 and D2 metrics, respectively. We also compared our proposed TransPCGC method with the Learned-PCGC [7] method on five datasets. Across all five datasets, our method showcases average BD-rate gains of 18.26% and 13.83% for D1 and D2 metrics, respectively. These results demonstrate the superior performance of our method compared to the comparative approaches across multiple datasets. In Figures 4 and 5, we provide a visual comparison between the original point cloud and the decompressed point clouds. To visualize the geometric details of the point cloud, we initially compute the normal vectors for each point using the surrounding 20 neighboring points and render the point cloud. This approach provides a superior visual effect. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Point Clouds**} & \multicolumn{6}{c}{**D1(p2 point)**} \\ \cline{2-7}  & **G-PCC(T)** & **G-PCC(O)** & **ADL-PCC [14]** & **GeoCNNv2 [6]** & **Learned-PGC [7]** & **MRM-PCGC [33]** & **VA-PCC [34]** \\ \hline Longders\_vox10 & \(-81.35\) & \(-97.33\) & \(-42.36\) & \(-43.08\) & \(-16.31\) & \(-5.98\) & \(-15.63\) \\ Loot\_vox10 & \(-79.47\) & \(-96.96\) & \(-40.42\) & \(-39.5\) & \(-14.9\) & \(-5.74\) & \(-15.06\) \\ Red\&black\_vox10 & \(-80.71\) & \(-92.04\) & \(-29.52\) & \(-44.58\) & \(-17.26\) & \(-1.68\) & \(-10.46\) \\ Soldier\_vox10 & \(-80.62\) & \(-95.48\) & \(-39.4\) & \(-43.42\) & \(-41.21\) & \(-5.11\) & \(-13.47\) \\ Queen\_vox10 & \(-79.19\) & \(-98.23\) & \(-34.1\) & \(-29.07\) & \(-9.73\) & - & - \\ Player\_vox11 & \(-81.76\) & \(-99.85\) & \(-41.63\) & \(-38.55\) & \(-15.07\) & - & \(-15.29\) \\ Dancer\_vox11 & \(-82.79\) & \(-99.71\) & \(-37.89\) & \(-34.89\) & \(-13.24\) & - & \(-14.78\) \\ Average & \(-80.84\) & \(-97.09\) & \(-37.9\) & \(-39.01\) & \(-14.39\) & \(-4.63\) & \(-14.12\) \\ \hline \hline \end{tabular}
\end{table}