# A Contrastive Learning Method for the Visual Representation of 3D Point Clouds
## 3 Experiment and Results
### Classification Performance  
The performance of unsupervised visual representation learning is reflected in the completion of downstream tasks. The model after learning is compared to the classification task, and the performance of the model is evaluated according to the classification accuracy. The digital capsule layer of the self-attention capsule network \(f(\cdot)\) is reconstructed into one-dimensional features as the input of the linear SVM classifier, and the SVM classifier is trained on ModelNet40. In order to compare the superiority of the classification performance of the proposed method, the commonly used unsupervised learning methods VConv-DAE [27], LatentGAN [7], FoldingNet [6], and PointCapsNet [20] are compared, as are the classical supervised methods PointNet [16] and PointNet++ [17]. The results in Table 1 show that the classification performance of the proposed method on ModelNet40 is better than that of the commonly used unsupervised methods, and it is close to classical supervised learning. Unsupervised visual representation learning may not be better than supervised learning of the model directly. In order to verify the effectiveness of the method in this paper, the backbone network is not compared and learned, and the linear SVM classifier directly performs supervised learning on ModelNet40. The results in Table 1 show that the backbone network can increase the classification accuracy by 3.3% after the contrastive learning training of this article, which shows that the contrastive learning of this model can effectively improve the quality of visual representation. The same method is used to train SVM on the different data sets of ShapeNetParts. ShapeNetParts contains 16,881 models in 16 categories, which are divided into 12,149 training samples and 2874 test samples, using only 20% of the training samples for training. The results in Table 2 show that the model achieves the advanced accuracy of shape classification in ShapeNetParts, which is an increase of 2.6% compared with the best comparison method, MulUnsupervised [1]. It shows that the visual representation obtained by contrast learning of the model can handle smaller data sets and can be better generalized to new tasks. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Unsupervised Pre-Training** & **Accuracy (\%)** \\ \hline VConv-DAE & ✓ & 75.5 \\ LatentGAN & ✓ & 85.7 \\ FoldingNet & ✓ & 88.4 \\ PointCapsule & ✓ & 88.9 \\ PointNet & - & 89.2 \\ PointNet++ & - & 90.7 \\ \hline Ours & - & 85.7 \\ Ours & ✓ & 89.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: ModelNet40 classification accuracy. Figure 5: The method described in this article. One of the main ways for studying unsupervised classification problems is to perform pre-training on a large amount of unlabeled data and perform transfer learning on a small amount of labeled data. The experiment is very consistent with this setting. The data volume of the unlabeled data set ShapeNet for contrastive learning is relatively large, with a sample size of about 57,000, while the data volume of the labeled ModelNet data set used for transfer learning is relatively small, with about 13,800 samples. Since it is usually difficult to obtain manually labeled data, we want to test how the performance of the model decreases when there is less labeled data. The ShapeNet data set is still used to train the contrastive learning network. Then, only a% of all training data is used in the ModelNet40 dataset to train the linear SVM, where a can be 1, 2, 5, 10, 20 and 30. The test data still uses all the data in the test data of the ModelNet data set. The results of this experiment are shown in Figure 6. It can be seen that when only 1% of the label samples are used, the classification accuracy is still above 50%.