# A Contrastive Learning Method for the Visual Representation of 3D Point Clouds
## 1 Introduction  
A point cloud is an interactive point set with an unchanged sparse order defined in coordinate space and samples from the surface of objects to capture their spatial semantic information [1]. Point clouds are obtained through 3D sensors (such as LiDAR scanners and RGB-D cameras). They can be used in human-machine interaction [2], automatic driving vehicles [3], and robot technology [4], and have a high practical application value. The manual labeling of point cloud targets is very expensive. Unsupervised visual representation learning can learn the effective visual representation of 3D point cloud targets without labeling information. Therefore, the unsupervised visual representation learning of point clouds [5] has become a research hotspot. The unsupervised visual representation learning of point clouds is mainly based on generative methods. Architecture such as autoencoders and generative adversarial networks have achieved success in learning the visual representation of point cloud targets and generating real samples from complex underlying distributions. [PERSON] et al. proposed FoldingNet [6] and trained an end-to-end depth autoencoder to directly use disorderedpoint clouds. A new decoding operation called folding is also proposed, which can achieve a low reconstruction error even for objects of fine structure, and theoretically shows that it is universal in point cloud reconstruction. [PERSON] et al. proposed LatentGAN [7], a self-encoder trained to learn a potential representation, and then trained a generation model, l-GAN, in a fixed potential space and realized shape editing through simple algebraic operations, such as semantic part editing, shape analogies and shape interpolation, and shape completion. This is easier to train than the original countermeasure generation network and achieves better reconstruction and coverage data distribution. However, the generative method of point clouds is trained by reconstructing the difference between the point cloud and the original point cloud. The generative method pays more attention to the details of each point, rather than abstract semantic information [8]. Point-based analysis usually assumes independence between each point, which reduces the ability to model correlations or complex structures. Discriminant methods in unsupervised representation learning use objective functions, similar to supervised learning, to learn visual representations. Compared with generative methods, discriminative methods focus more on the feature space rather than the details of each point. Discriminant methods generally train the network by customizing one or more pretext tasks [9] where the input and labels are from unlabeled data sets and the excuse task can provide powerful supervision signals for semantic information, thereby promoting learning semantic information. [PERSON] et al. [5] extracted random patch pairs from each image and trained a discriminant model to predict their relative positions in the image. Experiments show that using this feature representation of intra-image context prediction task learning can indeed capture the visual similarity between images. [PERSON] et al. [10] took recognizing the two-dimensional rotation of the input image as a pretext task to learn the semantic information on the image. Compared with the attention map generated by supervised learning method, it was proven that the region of interest in this method is the same as that of the supervised learning method. [PERSON] et al. [11] used a very large ResNet-101 network to jointly train the network of four different pretext tasks. Lasso regularization is also explored to encourage the network to decompose information on its representation and coordinate the network input in order to learn a more unified representation. The experimental results show that the deeper network effect is better and the performance can be improved even on a naive, multi-head architecture.