Research On Point Cloud Classification Method Based On Deep Learning

Point cloud classification is a challenging task in computer vision and deep learning due to its irregular and unstructured nature.With the growing availability of 3D data,point clouds have become an essential part of various applications such as virtual reality,augmented reality,robotics,and autonomous vehicles.Therefore,developing efficient and accurate methods that can effectively capture the unique features for point cloud classification has gained significant attention in recent years.This paper proposes three deep learning methods for point cloud classification,namely unsupervised contrastive learning based on point cloud transformation,masked autoencoders for self-supervised learning via point cloud completion,and dynamic hop distance prediction with graph Convolution networks for point cloud self-supervised learning.These methods are based on self-supervised learning,which means they do not require labeled data and can learn the latent feature from the raw point cloud data.These methods demonstrate promising results in point cloud classification and can be further developed for real-world applications.(1)Unsupervised contrastive learning based on point cloud transformationUnsupervised contrastive learning is a method of training deep neural networks without using labeled data.It involves learning representations of data that are invariant to variations within the data but differ across different points.This is achieved by training a network to differentiate between positive pairs and negative pairs.The contrastive loss function minimizes the distance between positive pairs and maximizes the distance between negative pairs.Specifically,a simple transformation is applied to the point cloud to create the augmented point cloud,then constructing the contrastive pairs.The contrastive pairs are fed into the network to differentiate between the transformed point cloud and the original point cloud.The network can be trained without labeled data and then be used for point cloud classification.The method exhibits excellent performance compared to other unsupervised methods and even outperforms some supervised methods on various benchmarks.(2)Masked autoencoders for point cloud completion via self-supervised learningMasked autoencoders method involves training an autoencoder to reconstruct the complete point cloud from a partial point cloud by masking out some of the points.This can improve the robustness of the network to missing data and improve the accuracy of point cloud classification tasks.The method contains three parts: patch feature embedding,masked autoencoders Transformer,and point cloud reconstruction.The patch feature embedding learns to encode the point cloud into a latent feature space,and the masked autoencoders Transformer aims to reconstruct the complete point cloud global feature from the limited input,finally using the multi-task folding operation to reconstruct the point cloud from generated global features.The method learns the point cloud patch semantic information via the relationship between visual patches and mask patches.It also improves the accuracy of the backbone networks and achieves better performance on various benchmarks.(3)Dynamic hop distance prediction with graph convolution networkHop distance prediction is a method of predicting the geometric distance and topologic connections between point cloud patches via their local features.The method utilizes the adjacency matrix of patches to learn the geometric structure and the contextual information of the point cloud.Then,using a graph attention mechanism to embed the low-level geometric distance into the high-level contextual features by assigning more attention to edge features between neighboring patches and less attention to distant patches,allowing edge features to contribute distinctively in aggregation.The method can improve the accuracy of point cloud classification tasks by exploiting the geometric structure of the point cloud explicitly.The results show that this method achieves the best performance than the state-of-the-art methods,demonstrating the effectiveness of the proposed self-supervised hop distance prediction task.In conclusion,point cloud classification is an essential task in computer vision and deep learning,but the three deep learning methods proposed in this paper demonstrate promising results and show the potential of unsupervised learning methods.These methods will become essential and can be further used in various applications,such as 3D scene object classificaiton,human pose recognition,autonomous driving dectection and virtual reality.