# An Efficient Bidirectional Point Pyramid Attention Network for 3D Point Cloud Completion
## 4 Experimental
### Completion Results on ShapeNet
The numbers in pairs are Pred \(\rightarrow\) GT and GT \(\rightarrow\) Pred Chamfer Distance \(\times\) 10\({}^{3}\) (lower is better). \begin{table}
\begin{tabular}{c c c c c} \hline
**Category** & \multicolumn{2}{c}{**PF-Net**} & \multicolumn{2}{c}{**BiPPAN**} \\ \cline{2-5}  & **Pred \(\rightarrow\) GT** & **GT \(\rightarrow\) Pred** & **Pred \(\rightarrow\) GT** & **GT \(\rightarrow\) Pred** \\ \hline Airplane & 1.084 & 1.119 & 0.963 & 0.872 \\ Bag & 3.979 & 4.668 & 2.83 & 2.624 \\ Cap & 5.254 & 4.897 & 4.004 & 3.614 \\ Car & 2.548 & 1.914 & 2.316 & 1.634 \\ Chair & 2.154 & 2.019 & 1.738 & 1.428 \\ Earphone & 6.003 & 8.058 & 3.194 & 3.690 \\ Guitar & 0.464 & 0.546 & 0.377 & 0.382 \\ Knife & 0.555 & 0.563 & 0.464 & 0.465 \\ Lamp & 4.943 & 3.883 & 3.863 & 2.557 \\ Laptop & 1.309 & 1.072 & 1.135 & 0.888 \\ Motorbike & 2.328 & 1.836 & 1.944 & 1.581 \\ Mug & 3.080 & 3.580 & 3.147 & 2.893 \\ Pistol & 1.284 & 1.053 & 0.920 & 0.811 \\ Rocket & 1.052 & 0.762 & 0.853 & 0.620 \\ Skateboard & 1.196 & 1.362 & 1.025 & 1.004 \\ Table & 2.305 & 2.123 & 1.760 & 1.629 \\ Mean & 2.471 & 2.466 & 1.908 & 1.668 \\ Params (\(\times\)10\({}^{6}\)) & 76.571 & & 11.763 \\ \hline \end{tabular}
\end{table} 
Table 2: Results of a comparison between our method and PF-Net on ShapeNet-16 dataset. We also provide the number of parameter (Params) in the last rows. Results on ShapeNet-55: To further investigate how PF-Net and our method performed with more dataset categories, we conducted experiments on ShapeNet-55. Due to the imbalanced number of shapes in the dataset, we classified the 55 categories into simple, moderate, and hard classes on the basis of their number. Specifically, more than 2500 training shapes were considered to be of the simple class, such as table, chair, and airplane. Fewer than 80 samples were classified as of the hard class, such as birdhouse, bag, and keyboard. Bed, camera, and rifle were classified as of the moderate class. We chose 3 categories from each class as examples to demonstrate the results. The average CD results for the three classes are shown in Table 3 with details. The category errors and average errors of our method were smaller than those of PF-Net. The results of our method on ShapeNet-55 were especially better than those on ShapeNet-13 and ShapeNet-16. Due to the imbalanced number of training samples, the average CD errors of the moderate and hard classes with insufficient samples for PF-Net were higher than those of the simple class. For our BiPPAN, there were no significant differences in the average errors of the three classes. In Figure 7, we present the qualitative results for 9 categories on Shapenet-55. As shown in the example, our method was able to complete the missing point cloud with higher accuracy and more details for the various incomplete categories. Regardless of quantitative comparison or qualitative analysis, our method could improve the quality of the completed point cloud on a diverse dataset and achieve good results in the point cloud completion task. Figure 6: Qualitative results on ShapeNet-16 showing the input point cloud (Input) and the ground truth (G.T. ), and the predictions of PF-Net and our model. Figure 7: Qualitative results on ShapeNet-55. Point cloud completion results of our method and PF-Net on some objects from the simple, moderate, and hard classes.