# GSV-NET: A Multi-Modal Deep Learning Network for 3D Point Cloud Classification
## 4 Experiment
### The Comparison on Sydney Datasets  
The outcomes of various state-of-the-art methods are available because the Sydney dataset is public. We accompany the etiquette to estimate the execution by employing the weighted F1 score. The etiquette is used by the authors of this dataset. The Sydney dataset is divided into four training/test folds with a subset of 588 models in 14 categories. Table 5 shows the performance comparison of various methods. Our framework obtains the most excellent performance among all chosen methods, and delivers a weighted F1 score of 0.798 (the F1 score for each class is reported in Table 6). The deep learning approaches defeat the handcrafted descriptor approaches, such as unsupervised feature learning ([PERSON] et al. [50]). GSV-NET exceeds VoxNet ([PERSON] and [PERSON] [18]), one of the first works using CNN for 3D data. In addition, our method outperforms other methods: NormalNet, BVCNNs, 3 DmFV-Net, and JointNet (0.74 for NormalNet, 0.755 for BVCNNs, 0.76 for 3 DmFV-Net, and 0.749 for JointNet). ORION method ranks second place with a score of 0.778. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Class Name**} & \multicolumn{2}{c}{**Accuracy**} & \multirow{2}{*}{**Class Name**} & \multicolumn{2}{c}{**Accuracy**} & \multirow{2}{*}{**Class Name**} & \multirow{2}{*}{**Class Name**} & \multicolumn{2}{c}{**Accuracy**} \\ \cline{2-2} \cline{5-10}  & **EEM** & & & & **EEM** & & & **Ours** & & **EEM** & **Ours** \\ \hline airplane & 100 & 100 & cup & 65 & 75 & laptop & 100 & 100 & sofa & 98 & 98 \\ bathtub & 90 & 84 & curtain & 85 & 95 & mantel & 96 & 97 & stairs & 85 & 80 \\ bed & 99 & 99 & desk & 89 & 93 & monitor & 97 & 100 & stool & 85 & 95 \\ bench & 75 & 90 & door & 95 & 85 & night\_stand & 81 & 79 & table & 77 & 94 \\ bookshelf & 97 & 97 & dresser & 81 & 87 & person & 80 & 90 & tent & 95 & 95 \\ bottle & 94 & 96 & flower\_pot & 20 & 20 & piano & 87 & 95 & toilet & 99 & 98 \\ bowl & 100 & 100 & glass\_box & 94 & 96 & plant & 79 & 83 & tv\_stand & 88 & 88 \\ car & 96 & 100 & guitar & 100 & 100 & radio & 50 & 85 & vase & 78 & 81 \\ chair & 96 & 96 & keyboard & 95 & 100 & range\_hood & 90 & 94 & wardrobe & 65 & 75 \\ cone & 90 & 100 & lamp & 75 & 95 & sink & 75 & 80 & xbox & 75 & 90 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Individually category accuracy in the point cloud dataset: ModelNet40. \begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**Method**} & \multicolumn{1}{c}{**F1 Score**} \\ \hline UFL + SVM [50] & 0.67 \\ \hline BV-CNNs [59] & 0.755 \\ \hline VoxNet [18] & 0.72 \\ \hline NormalNet [60] & 0.74 \\ \hline ORION [61] & 0.778 \\ \hline JointNet [51] & 0.749 \\ \hline
3 DmFV-Net [20] & 0.76 \\ \hline GSV-NET & 0.798 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: F1 score obtained by various techniques on the LiDAR dataset: Sydney Urban Objects. Numerical results verified the superior performance of the GSV-NET. Our framework handles the drawback of the multimodal MIFN and MVCNN methods. Moreover, GSV-NET reduces the training time compared with MIFN and MVCNN methods. The total training time of GVS-NET is 12.4 h, including 4.6 h for 3D CNN, 2.4 h for each 2D CNN, and 0.2 h for each 1D CNN (\(4.6+2.4\times 3+0.2\times 3=12.4\)). The authors who introduced MVCNN and MIFN in the original paper did not mention the training time. We use the experiment results of the NVIDIA company [62] to estimate the training time of MIFN. The NVIDIA research reports the training time for ModelNet40 with the PointNet model more than 6 h and a five-views MVCNN model at 4 h (12 GB memory GPU). As a result, the total training time of the MIFN is more than 14 h, including 6 h for the PointNet model, 4 h for 12-views MVCNN, and 4 h for 12 panorama views MVCNN (\(6+4+4=14\)). Another improved version of the MVCNN method, Transformation Correction Network (TCN-MVCNN), took 15 h to complete the training on the ModelNet40 dataset with 12 views for each 3D point cloud (see reference [62]).