# Par3 DNet: Using 3 DCNNs for Object Recognition on Tridimensional Partial Views
## 4 Experimentation
### Results on the ModelNet10 Dataset  
We fed the test samples to the architecture and we got the results shown in Table 1. The global accuracy in this case is 78.39%. As shown in the figure, the bathtub class only got 59% accuracy. This is understandable as this is by far the category with the fewest number of samples in the dataset. This fact is, thus, clearly reflected in the results. The desk and table categories yield bidirectional confusions. This is also expected as both categories depict barely the same topological data. In this case, the desk category achieves 41% accuracy, but the 23% of the samples were labeled as desk. The desk category also has a low number of samples compared with the table one, so this also explains the confusion. There is also a slight confusion between the nightstand and dresser categories for the same reason as that for the desk and table categories. The best performer is the chair category. This is due to the chair category being the one with the highest number of samples in the dataset. Figure 6: Number of samples in the dataset. The Table 2 shows the accuracy per category and view. As shown, there is no view with constant low accuracy across all the categories. We did this experiment to check that all the views we selected are actually interesting to perform an accurate classification. Nonetheless, there are isolated combination of views and object that are particularly error prone. This is the case of the view 9 for the bathtub and desk, or the view 19 for the desk. We also computed the top-3 accuracy. This metric considers that a sample is correctly classified if the ground truth class if among the three predictions with the higher score. Taking this into account, we  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & **Bathtub** & **Bed** & **Chair** & **Desk** & **Dresser** & **Monitor** & **Nightstand** & **Sofa** & **Table** & **Toilet** \\ \hline
1 & 75 & 75 & 95.7 & 48.6 & 64.3 & 97.5 & 68.8 & 62.7 & 78.9 & 91.1 \\ \hline
2 & 66.7 & 76.5 & 84.2 & 48.1 &  
achieved a 94.36% accuracy. This experiment confirms also that, in the cases of bidirectional confusions discussed before, the second or third guess is usually the right one. The receiver operating characteristic (ROC) curves and area under the curve (AUC) for each category are reported in Figure 7. The ROC curves shows the sensitivity against the 1-specificity, so a perfect classifier would provide the correct class under any detection threshold. As the analysis shows, the classifier is performing nicely on every class, providing 0.8 of true positives rate under a false positive rate of 0.1. Only the category \"desk\" is not following this trend because, as discussed before, its samples tend to be classified as \"table\". The area under the curve confirms the accuracy of Par3 DNet considering that an AUC of 1 means that the classifier is perfect. We also compared our system with PointNet [38]. Despite this approach is intended to be fed with complete representations of the objects, we retrained the architecture following the same protocol we used to train Par3 DNet. Namely, we retrained the architecture with the partial views of the objects and then tested it. PointNet achieved a top-1 accuracy of 71.25% and a top-3 accuracy of 88.93%, being outperformed by Par3 DNet that, as mentioned earlier, achieved 78.39% and 94.36%. It is also worth noting that the number of input points for PointNet is fixed, so it cannot be fed with different resolution point clouds. However, Par3 DNet can be fed with point clouds composed of an arbitrary number of points. It should be noted that our method takes as an input a partial view of the object. This fact makes the comparison unfair with the participants of the ModelNet10 challenge as they consider the full object. So, to enable an easy and fair comparison with the approaches submitted to the challenge, we adapted our method to work on full objects.