# Point-Sim: A Lightweight Network for 3D Point Cloud Classification
## 4 Experiments
### Shape Classification Task on ModelNet40 Dataset  
Dataset: We evaluated our method on the ModelNet40 dataset for the classification task. This dataset comprises a total of 12,311 CAD mesh models, with 9843 models assigned for training and 2468 models for testing. The dataset covers 40 different classes.  
In order to optimize memory usage and improve computational speed, we followed the experimental configuration of PointNet [7]. We uniformly selected 1024 points from the mesh surface using only the 3D coordinates as input data. We used the overall accuracy (OA) and the number of parameters for evaluation.  
For the parametric network, we applied data augmentation; the data were augmented by adding jitter, point random dropout, and random scale scaling to each coordinate point of the object, where the mean value of jitter is 0, and its standard deviation is 0.1. The random scale scaling was between 0.66 and 1.5, and the probability of each point dropping out ranged from 0 to 0.875. The data were augmented with a weight decay of 0.0001 using an initial learning rate of 0.003 for the Adam optimizer with an initial learning rate of 0.001, and a weight decay of 0.0001 was used. In addition, training was performed using crossentropy loss. The batch size set for training was 32, and the maximum epoch was set to 300.  
Experimental Results: The classification results on ModelNet40 are shown in Table 1. We compared our results with some recent methods on a RTX 3090 GPU. This comparison signifies that our proposed model generally outperformed several other models. We compared our results with respect to overall accuracy (OA), the number of parameters (Params), training time, and test speed (samples/second) with some recent methods. The proposed nonparametric method achieved an OA of 83.9% with 0 M parameters and without any training time, and the proposed parametric method achieved an OA of 93.3% with 0.8 M parameters, while our light parametric model test speed reached 926 samples  
Figure 5: Naive feature similarity matching.  
per second. And because of the Squash module, our model was able to converge in a relatively short time of 3.1 h. Based on these comparisons with our method and related works, we have reached the conclusion that the network has advantages in terms of training speed and accuracy, as well as device requirements.  
Our results are visualized on the ModelNet40 dataset, and the results are shown in Figure 6. For the nonparameterized model Point-Sim, the model OA was improved compared to Point-NN with similar inference speed. For the parameterized model Point-SimP, it was able to greatly improve the inference speed while maintaining the accuracy and had an advantage in the network training time.  
We generated a 40 x 40 confusion matrix for our classification results, and the results are shown in Figure 7, in which there are 40 categories, with the horizontal axis representing the predicted labels and the vertical axis representing the ground truth labels (including airplane, bathtub, bed, bench, etc.). By visualizing the confusion matrix, we can see that most of the categories were classified well; for example, all the classifications on label 1 (airplane) and label 19 (keyboard) are correct, but the accuracies on label 16 (flower pot) and label 32 (stairs) still need to be improved. Figure 8 shows some representative results on ModelNet40.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Overall Accuracy (\%)** & **Parameters** & **Train Time** & **Test Speed** \\ \hline PointNet & 89.2 & 3.5 M & - & - \\ PointNet++ & 90.7 & 1.7 M & 3.4 h & 521 \\ GBNet & 93.8 & 8.4 M & - & 189 \\ DGCNN & 92.9 & 1.8 M & 2.4 h & 617 \\ PointMLP & 94.1 & 12.6 M & 14.4 h & 189 \\ Point-NN & 81.80 & 0 M & 0 & 275 \\ \hline Point-Sim & 83.9 & 0 M & 0 & 231 \\ Point-SimP & 93.3 & 0.8 M & 3.1 h & 962 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Classification results on ModelNet40.  
Figure 6: Visualization results on the Modenet40 dataset.