# GSV-NET: A Multi-Modal Deep Learning Network for 3D Point Cloud Classification
## 4 Experiment
### The Comparison on ModelNet40 Dataset
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Methods**} & \multicolumn{2}{c}{**Methods**} & \multicolumn{2}{c}{**Methods**} & \multicolumn{2}{c}{**Methods**} \\ \cline{3-11} \cline{3-11} \multicolumn{1}{c}{**Class Name**} & \multirow{3}{*}{**Class Name**} & \multirow{3}{*}{**Class Name**} & \multirow{3}{*}{**Class Name**} & \multirow{3}{*}{**Class Name**} & \multirow{3}{*}{**Class Name**} & \multirow{3}{*}{**Class Name**} & \multirow{3}{*}{**Class Name**} & \multirow{3}{*}{**Class Name**} & \multirow{3}{*}{**Class Name**} & \multirow{3}{*}{**Class Name**} \\ \hline \multirow{5}{*}{**Class Name**} & airplane & 1.00 & 1.00 & cup & 0.75 & 0.75 & laptop & 1.00 & 1.00 & sofa & 0.96 & 0.98 \\  & bathtub & 0.92 & 0.84 & curtain & 0.85 & 0.95 & mantel & 0.96 & 0.97 & stairs & 0.95 & 0.80 \\  & bed & 0.96 & 0.99 & desk & 0.92 & 0.93 & monitor & 0.99 & 1.00 & stool & 0.80 & 0.95 \\  & bench & 0.80 & 0.90 & door & 0.80 & 0.85 & night\_stand & 0.71 & 0.79 & table & 0.72 & 0.94 \\  & bookshelf & 0.97 & 0.97 & dresser & 0.73 & 0.87 & person & 0.90 & 0.90 & tent & 0.95 & 0.95 \\  & bottle & 0.93 & 0.96 & flower\_pot & 0.35 & 0.20 & piano & 0.95 & 0.95 & toilet & 1.00 & 0.98 \\  & bowl & 0.95 & 1.00 & glass\_box & 0.93 & 0.96 & plant & 0.79 & 0.83 & tv\_stand & 0.70 & 0.88 \\  & car & 0.98 & 1.00 & guitar & 1.00 & 1.00 & radio & 0.85 & 0.85 & vase & 0.79 & 0.81 \\  & chair & 0.96 & 0.96 & keyboard & 1.00 & 1.00 & range\_hood & 0.94 & 0.94 & wardrobe & 0.75 & 0.75 \\  & cone & 1.00 & 1.00 & lamp & 0.85 & 0.95 & sink & 0.85 & 0.80 & xbox & 0.80 & 0.90 \\ \hline \multirow{5}{*}{**Class Name**} & airplane & 1.00 & 0.98 & cup & 0.60 & 0.65 & laptop & 0.91 & 1.00 & sofa & 0.98 & 1.00 \\  & bathtub & 0.96 & 1.00 & curtain & 0.81 & 0.86 & mantel & 0.98 & 0.95 & stairs & 1.00 & 0.76 \\  & bed & 0.98 & 0.95 & desk & 0.75 & 0.89 & monitor & 0.96 & 0.98 & stool & 0.89 & 0.70 \\  & bench & 0.64 & 0.90 & door & 0.84 & 0.89 & night\_stand & 0.78 & 0.86 & table & 0.83 & 0.91 \\  & bookshelf & 0.93 & 0.97 & dresser & 0.78 & 0.86 & person & 1.00 & 1.00 & tent & 0.83 & 0.76 \\  & bottle & 0.95 & 0.95 & flower\_pot & 0.19 & 0.17 & piano & 0.97 & 0.97 & toilet & 0.99 & 1.00 \\  & bowl & 0.83 & 0.74 & glass\_box & 0.99 & 0.98 & plant & 0.90 & 0.86 & tv\_stand & 0.63 & 0.94 \\  & car & 1.00 & 1.00 & guitar & 1.00 & 1.00 & radio & 0.68 & 0.71 & vase & 0.83 & 0.87 \\  & chair & 0.95 & 0.98 & keyboard & 0.95 & 1.00 & range\_hood & 0.99 & 0.99 & wardrobe & 0.63 & 0.94 \\  & cone & 0.95 & 0.95 & lamp & 1.00 & 0.86 & sink & 0.89 & 0.84 & xbox & 0.89 & 0.86 \\ \hline \multirow{5}{*}{**Class Name**} & airplane & 1.00 & 0.99 & cup & 0.67 & 0.70 & laptop & 0.95 & 1.00 & sofa & 0.97 & 0.99 \\  & bathtub & 0.94 & 0.91 & curtain & 0.83 & 0.90 & mantel & 0.97 & 0.96 & stairs & 0.97 & 0.78 \\  & bed & 0.97 & 0.97 & desk & 0.83 & 0.91 & monitor & 0.98 & 0.99 & stool & 0.84 & 0.81 \\  & bench & 0.71 & 0.90 & door & 0.82 & 0.87 & night\_stand & 0.74 & 0.82 & table & 0.77 & 0.93 \\  & bookshelf & 0.95 & 0.97 & dresser & 0.75 & 0.87 & person & 0.95 & 0.95 & tent & 0.88 & 0.84 \\  & bottle & 0.94 & 0.96 & flower\_pot & 0.25 & 0.19 & piano & 0.96 & 0.96 & toilet & 1.00 & 0.99 \\  & bowl & 0.88 & 0.85 & glass\_box & 0.96 & 0.97 & plant & 0.84 & 0.85 & tv\_stand & 0.66 & 0.91 \\  & car & 0.99 & 1.00 & guitar & 1.00 & 1.00 & radio & 0.76 & 0.77 & vase & 0.81 & 0.84 \\  & chair & 0.96 & 0.97 & keyboard & 0.98 & 1.00 & range\_hood & 0.96 & 0.96 & wardrobe & 0.68 & 0.83 \\  & cone & 0.98 & 0.98 & lamp & 0.92 & 0.90 & sink & 0.87 & 0.82 & xbox & 0.84 & 0.88 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Individual category precision, recall, and F1 score in the point cloud dataset: ModelNet40. Only the EEM method provides accuracy for each class on the ModelNet40 dataset. We report the detailed comparison for each category in Table 4. Compared with the EEM approach, our method has 24, 11, and 5 classes with higher accuracy, the same accuracy, and lower accuracy, respectively.