# An Efficient Bidirectional Point Pyramid Attention Network for 3D Point Cloud Completion
## 5 Conclusions  
In this paper, we recommended a novel Bidirectional Point Pyramid Attention Network framework (BiPPAN) for point cloud completion. Our method produces missing point clouds via an encoder-decoder structure. The attention module was introduced into the encoder and decoder to raise the correlational information of point clouds, which extracts the local details of object features better [31]. The point pyramid attention decoder was designed not only to fully use hierarchical features from the bidirectional connection, but also to capture long-distance structural information by employing skip connections as well [32]. Experimental results on ShapeNet-13, ShapeNet-16, and ShapeNet-55 demonstrate that, no matter the size of the dataset, our proposed method was effective and efficient in comparison with other methods. An ablation and robustness test validated our claim that the attention module, bidirectional connection, and skip connections could more effectively extract and exploit features of point clouds to improve prediction accuracy. Our method tended to reduce the error in challenging classes.  
Hence, this method can be widely applied to the task of generating target point clouds with both rich semantic profiles and detailed features while preserving existing contours. The good results of BiPPAN on many datasets demonstrate its ability to repair and complete 3D shapes, and improve the accuracy of downstream tasks such as 3D recognition, and 3D object detection and tracking. As a point cloud preprocessing method, our method has tremendous potential in autonomous vehicles, 3D reconstruction, and remote sensing research.  
Methodology and writing--original draft preparation, Y.L.; data curation and visualization, Y.X.; investigation and supervision, J.G.; conceptualization and project administration, Q.Y. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Missing Ratio** & **25\%** & \multicolumn{3}{c}{**50\%**} & \multicolumn{3}{c}{**75\%**} \\ \hline CD (\(\times 10^{3}\)) & Pred \(\rightarrow\) GT & GT \(\rightarrow\) Pred & Pred \(\rightarrow\) GT & GT \(\rightarrow\) Pred & Pred \(\rightarrow\) GT & GT \(\rightarrow\) Pred \\ Airplane & 0.963 & 0.872 & 0.971 & 0.809 & 0.968 & 0.896 \\ Car & 2.316 & 1.634 & 2.494 & 1.786 & 2.479 & 1.841 \\ \hline \end{tabular}
\end{table}
Table 5: Robustness results of different extent of incomplete input point cloud using our method.  
Figure 9: Examples of point cloud completion results when the input point cloud misses (**a**) 25%, (**b**) 50%, and (**c**) 75% of the original point cloud. Gray and blue denote the input incomplete point cloud and predicted missing point cloud, respectively. Yellow represents the real point cloud.  
**Funding:** This research was funded by the Young Science and Technology Star Project of Dalian, China [grant number 2022 RQ092], and the Technology Innovation Fund Project of Dalian Neusoft University of Information [grant number TIFP202303].  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Publicly available datasets were analyzed in this study. The datasets can be found here: [[https://www.shapenet.org/](https://www.shapenet.org/)]([https://www.shapenet.org/](https://www.shapenet.org/)) (accessed on 17 November 2022).  
**Acknowledgments:** The authors acknowledge all editors and reviewers for their suggestions. We would also like to thank the Dalian Ascent AI Computing Center and Dalian Ascent AI Ecosystem Innovation Center for providing computing power and technical support.  
**Conflicts of Interest:** The authors declare no conflict of interest.