# A Contrastive Learning Method for the Visual Representation of 3D Point Clouds
## 1 Introduction
As a discriminative method, contrastive learning based on latent space [12] can effectively learn semantic similarity and can solve the problem of ignoring abstract semantic information learning by generative methods [8]. Recently, contrastive learning has shown great prospects, and unprecedented achievements have been made in two-dimensional visual representation learning. [PERSON] et al. proposed a comparative learning method, SimCLR [8]. On ImageNet, the trained linear classifier is close to supervised. The performance of ResNet50 reaches a top-1 accuracy rate of 76.5% and even surpasses some supervised learning methods on some data sets. However, the SimCLR method relies on a huge batch of data and a deeper network, which consumes computing resources. [PERSON] et al. proposed a contrastive learning method, MoCo [13], which uses contrastive learning as a way to look up a dictionary. It constructs a dynamic dictionary with a queue and a moving average encoder, which makes it possible to construct a large and consistent dictionary that is conducive to training small batches of data and promotes the development of contrastive learning. [PERSON] et al. [14] proposed PointContrast, which performs contrastive learning on a large number of three-dimensional point cloud scene data sets and achieves the best results of segmentation and classification on six different benchmarks of indoor and outdoor and real and synthetic data sets, and experiments show that the learned representations can generalize across domains. In the field of two-dimensional images, ResNet [15] is mainly used as the backbone network of the contrast learning framework for feature extraction. Due to the disorder of point clouds, the ordering of points does not affect the properties of objects.The network of extracting point cloud features must be a symmetric function, so the commonly used ResNet is not suitable for direct application to point cloud. [PERSON] et al. proposed PointNet [16], which uses a symmetric function to solve the problem of point cloud disorder. Subsequently, [PERSON] et al. proposed PointNet++ [17], which extracts local features of fine geometric structures from the neighborhood and solves the problem of extracting local features, which PointNet cannot do. The proposal of PointNet++ has caused deep learning methods based on this work to widely appear in three-dimensional point cloud processing. Most of the unsupervised visual representation learning frameworks of point clouds also use networks similar to PointNet++ as the backbone network, such as LatentGAN [7], 3 DAAE [18], So-Net [19], and other methods. However, the scalar features used by PointNet++ do not contain the spatial arrangement information in the point cloud data and do not consider the geometric relationship between parts [20], which is very important for the interpretation and description of three-dimensional shapes. The capsule network proposed by [PERSON] et al. [21] replaces the scalar features of CNN with vector features. The vector features save the feature information of different dimensions through a dynamic routing method, which can preserve the geometric relationship between components in the image. However, the dynamic routing in the capsule network will damage the robustness of the input affine transformation [22]. The input of the comparative learning framework generates positive and negative sample pairs through affine transformation. Therefore, the dynamic routing in the capsule network will affect the performance of contrastive learning. To solve the above problems, this paper proposes a contrastive learning method for three-dimensional point cloud visual representation. Combining the ideas of PointNet++ and the capsule network, a self-attention point cloud capsule network is designed as the backbone network of the contrastive learning framework.