# TransPGC: Point Cloud Geometry Compression Based on Transformers
## 5 Conclusions  
In this paper, we introduce a Transformer-based point cloud geometric compression network. It includes preprocessing modules for voxelization and partitioning of point clouds, compression networks to achieve optimal BD-Rate, and postprocessing modules for point cloud reconstruction. This approach addresses the common problem in mainstream point cloud geometric compression algorithms, which often overlook global information in point cloud data. We propose a low-complexity and low-computation Transformer module, incorporating a pooling operation that does not require any learnable parameters as a token mixer to compute long-distance dependencies. This method ensures global feature extraction while significantly reducing both computations and parameters. In addition, we employ the convolutional layer for feature extraction, which further reduces the number of parameters while preserving the spatial structure of the point cloud. The experimental results demonstrate that our method improves the reconstruction quality of the point cloud and preserves more details. Moreover, our method outperforms other compared algorithms in terms of BD rate gain. Compared with the data from the latest VA-PCC published paper, our method achieves an average BD-Rate gain of 14.12% and 15.99% for D1 and D2 distortion criteria, respectively. Furthermore, when compared with the Learned-PCGC network, our method shows improvement in BD-Rate while reducing encoding and decoding time by 16% and model size by 50%. There are numerous interesting topics for exploration in the future, including (1) enhancing the entropy coding module by constructing more accurate context probability models using the Transformer module, (2) attribute compression (e.g., RGB colors), and (3) motion capturing for dynamic point clouds. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **G-PCC(O) [20]** & **G-PCC(T) [20]** &
\begin{tabular}{c} **Learned-** \\ **PCGC [7]** \\ \end{tabular} & **Ours** \\ \hline Encoding time & 1.6 & 8.16 & 6.63 & 7.42 \\ Decoding time & 0.6 & 6.58 & 17.49 & 12.85 \\ Model size & 2.6 M & 2.6 M & 8.0 M & 4.0 M \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Compare the Enc time, Dec time, and Model size of different methods (unit: second/s). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ablation** & **Variant** & **Bpp** & **D1** & **D2** \\ \hline Baseline & [Trans, Trans, Trans] & 0.59 & 75.45 & 79.53 \\ \hline Hybrid Stages & \begin{tabular}{c} [Trans, Trans, Trans] \(\rightarrow\) [Trans, Trans*2, Trans] \\ [Trans, Trans, Trans] \(\rightarrow\) [Trans, Trans*3, Trans] \\ \end{tabular} & 0.58 & 75.48 & 79.57 \\ \hline Activation & \begin{tabular}{c} GeLU \(\rightarrow\) ReLU \\ GeLU \(\rightarrow\) Switch \\ \end{tabular} & 0.55 & 75.45 & 79.58 \\ \hline Normalization & \
\begin{tabular}{c} Group Normalization \(\rightarrow\) Layer Normalization \\ Group Normalization\(\rightarrow\) Batch Normalization \\ \end{tabular} & 0.54 & 75.22 & 79.36 \\ \hline Token mixers &
\begin{tabular}{c} Pooling size 7\(\rightarrow\)5 \\ Pooling size 7\(\rightarrow\)9 \\ \end{tabular} & 0.55 & 75.47 & 79.62 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Ablation for Transformer Blocks on the “longdress” point cloud. **Author Contributions:** Conceptualization, S.L. and H.Y. ; data curation, H.Y. and C.H. ; formal analysis, S.L. ; funding acquisition, H.Y. and C.H. ; investigation, S.L. ; methodology, S.L. and C.H. ; project administration, H.Y. and C.H. ; resources, H.Y. and C.H. ; writing--original draft, S.L. ; writing--review and editing, S.L. and C.H. All authors have read and agreed to the published version of the manuscript. **Funding:** This work has been partially supported by the National Key R&D Program of China under grant No. 2020 YFB1709200. **Data Availability Statement:** The dataset used in this paper is the publicly available shapenet, ModelNet40, MVUB, 8 iVFB, and Owli.