# GSV-NET: A Multi-Modal Deep Learning Network for 3D Point Cloud Classification
## 4 Experiment
### The Comparison on ModelNet40 Dataset  
We test GSV-NET on the ModelNet40 dataset with different approaches on four main data representations (voxel, point, graph, and image). Table 2 shows the recognition outputs of all methods, and our proposed framework obtains the most excellent performance, with a classification rate of 92.7% for ModelNet40. GSV-NET outperforms a pioneering approach, PointNet, using the natural point cloud. The symmetric max-pooling function is applied to obtain a global descriptor. However, PointNet does not capture the local structure because it does not examine the local dependency between points, leading to an accuracy rate of 89.2%. To handle the drawback of PointNet, PointNet++ splits the point cloud into different regions to obtain the local descriptor and increases the accuracy rate to 90.7%. Another method, PointGCN, obtains local descriptors by the kNN graph but neglects global connections among these local descriptors. It has classification results of 89.5%. The best performance method in the point cloud group is the 3 DmFV method, having a precision of 91.6% (3 DmFV-Net). Although performing well, voxel-based methods consume high memory due to voxels' sparsity, resulting in wasted calculations for convolution across the non-occupied areas. The memory usage also restricts the voxel presentation, commonly in the range of 32 to 64 cubes. Therefore, VoxNet, 3D-A-Nets, and 3 DShapeNets produce low accuracy of 83%, 90.5%, and 77.32%, respectively. Multiview-based approaches (MVCNN) achieve a better result than voxel-based methods because they apply a well-known 2D CNN, resulting in high accuracy of 90.1%. Unlike the multi-view or voxel-based approach, the graph-basedmethod fits the irregular data processing like 3D shape classification. The graph-based method takes advantage of the sparsity of the point cloud. However, a challenging and further study is to design efficient convolution, pooling modules for graph-based networks. There is still a gap between the Reeb graph and the multiview-based approach such as MVCNN. Hence, the Reeb graph gives a precision of 89.9%. The 3D point cloud provides objects' information suitable for classifying shapes with diverse scales, while the 2D image gives much texture data better for specifying shapes with indistinguishable appearances [56; 57]. The merging of these two types of information is useful to improve the representation of the extracted features for classification purposes. MIFN merges the 3D information that comes from PointNet architecture and the 2D data that come from MVCNN. However, the MVCNN takes only the projection of the 3D models without the regional information. In contrast, GSV-NET captures not only global representation, but also regional representation. Moreover, GSV-NET uses 2D point locations and does not depend on a camera setting, while MVCNN requires the camera setting. As a result, GSV-NET has an accuracy of 92.7% (point cloud + three views) while MIFN reaches 90.83% (point cloud + 12 views) and 91.86% (point cloud + 12 views + panorama-views). GSV-NET effectively fuses both 3D global and 2D region features, and therefore shows superior performance compared with other approaches. We describe the statistical outcomes of every class for the classification task in detail. Figure 11 presents the ratio between correctly classified models and the total number of models for each class. The flower pot is misclassified as plants at 55% and as vases at 25%, making it the most misclassified category at 80% in total. None of the methods in Table 2 provides the full confusion matrix. We use the confusion matrix of the PointNet++ obtained from reference [58] to calculate the precision, the recall, and the F1 score. Reference [58] follows all steps in the PointNet++ approach and uses the same parameters given by the authors of the PointNet++ method. The detailed comparison for each category is given in Table 3.  
Overall, GSV-NET has more classes with higher performance than the PointNet++ approach.