# TransPGC: Point Cloud Geometry Compression Based on Transformers
## 4 Experiment
### Experiment Results

Table 1: BD-rate reduced G-PCC(T) [20], G-PCC(O) [20], ADL-PCC [14], GeoCNNv2 [6], Learned-PCC [7], MRM-PCGC [33] and VA-PCC [34] in D1 based BD-rate measurement (percentage/%). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Point Clouds**} & \multicolumn{6}{c}{**D2(p2 plane)**} \\ \cline{2-9}  & **G-PCC(T)** & **G-PCC(O)** & **ADL-PCC [14]** & **GeoCNNv2 [6]** & **Learned-PCC [7]** & **MRM-PCGC [33]** & **VA-PCC [34]** \\ \hline Longders\_vox10 & \(-76.56\) & \(-96.95\) & \(-41.07\) & \(-36.26\) & \(-12.24\) & \(-12.19\) & \(-17.79\) \\ Loot\_vox10 & \(-74.84\) & \(-96.41\) & \(-39.5\) & \(-32.37\) & \(-13.53\) & \(-11.22\) & \(-16.37\) \\ Red\&black\_vox10 & \(-75.28\) & \(-94.24\) & \(-30.47\) & \(-33.16\) & \(-13.17\) & \(-9.21\) & \(-14.99\) \\ Soldier\_vox10 & \(-75.97\) & \(-95.25\) & \(-37.8\) & \(-36.57\) & \(-12.95\) & \(-10.51\) & \(-15.82\) \\ Queen\_vox10 & \(-75.27\) & \(-98.78\) & \(-38.07\) & \(-20.81\) & \(-11.78\) & - & - \\ Player\_vox11 & \(-82.66\) & \(-99.99\) & \(-47.06\) & \(-40.72\) & \(-21.62\) & - & \(-15.48\) \\ Dancer\_vox11 & \(-82.52\) & \(-99.98\) & \(-47.95\) & \(-42.58\) & \(-31.3\) & - & \(-15.51\) \\ Average & \(-77.59\) & \(-97.37\) & \(-40.27\) & \(-34.64\) & \(-16.66\) & \(-10.78\) & \(-15.99\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: BD-rate reduced G-PCC(T) [20], G-PCC(O) [20], ADL-PCC [14], GeoCNNv2 [6], Learned-PCCGC [7], MRM-PCGC [33] and VA-PCC [34] in D2 based BD-rate measurement (percentage/%). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Point Clouds**} & \multicolumn{6}{c}{**G-PCC(O) [20]**} & \multicolumn{2}{c}{**Learned-PCC [7]**} \\ \cline{2-5}  & **D1** & **D2** & **D1** & **D2** \\ \hline
8 iVFB & \(-96\) & \(-96.32\) & \(-15.13\) & \(-13.08\) \\ Owili & \(-78.03\) & \(-75.55\) & \(-13.45\) & \(-29.4\) \\ MVUB & \(-83.34\) & \(-68.85\) & \(-9.86\) & \(-2.53\) \\ ModelNet & - & - & \(-24.57\) & \(-12.06\) \\ ShapeNet & - & - & \(-28.27\) & \(-12.1\) \\ Average & \(-85.79\) & \(-80.24\) & \(-18.26\) & \(-13.83\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: BD-rate reduced G-PCC(O) [20] and Learned-PCC [7] in D1 and D2 based BD-rate measurement (percentage/%). Additionally, we generate error maps based on the D1 error between the decompressed and original point cloud, allowing us visualization of the error distribution. Comparing the resulting point clouds from different compression methods, our method stands out in preserving intricate details and generating visually superior point clouds, as indicated by the red and blue dashed boxes in Figures 4 and 5. It is noteworthy that G-PCC (T) [20] introduces noticeable holes, while both G-PCC (O) [20] and G-PCC (T) [20] exhibit sparser reconstructed point clouds with significant quantization loss compared to other methods. This phenomenon occurs because octree-based encoding methods often lose details in leaf nodes at low bit rates. On the contrary, ADL-PCC [14] and Learned-PCC [7] demonstrate better preservation of details and fill a significant number of holes. However, our method surpasses them in terms of detail preservation. In Table 4, we compare the running time of different methods. The table displays the encoding and decoding time as well as the storage space of each method. It is evident that the traditional method G-PCC(O) [20] has the fastest encoding and decoding time, resulting in a 16% reduction in encoding and decoding time and a 50% reduction in storage space compared to the Learned-PCGC [7]. The slightly slower decoding time of our method is primarily attributed to the generation of more voxels in the reconstruction process, which  
Figure 4: Visual comparison of “longdress” for Ground Truth, Ours, Learned-PCGC [7], ADL-PCC [14], G-PCC (T) [20] and G-PCC (O) [20]. Figure 5: Visual comparison of “Soldier” for Ground Truth, Ours, Learned-PCGC [7], ADL-PCC [14], G-PCC (T) [20] and G-PCC (O) [20]. is based on a binary classification method. We plan to further optimize this aspect in future work.