Point cloud learning has attracted more and more attention due to its wide application in many scientific fields,such as computer vision,automatic driving,and robot technology.The sparseness,disorder and finiteness of point cloud data present great difficulties to the point cloud classification task based on deep convolutional neural networks.The convolutional neural network often uses operations like voxelization and multi-view.However,the conversion process causes problems of local feature information loss and low computational efficiency.At present,the existing deep learning method oriented point cloud classification which consumes the raw point cloud has the problem of many traning parameters and complex model structures,thus making it difficult to process the real-time task.In order to lightweight the network to realize real-time point cloud classification tasks,on the basis of intensive studies of these problems,an index edge geometric convolution neural network model is proposed in this paper.First,in order to achieve lightweight,the network structure and hyperparameters are trimmed and compressed,respectively.Second,to enhance the accuracy and efficiency of algorithms,a new local region is determined on each convolution layer by using the KNN(K-Nearest Neighbor) algorithm,with the vector direction between adjacent points added,and the output features of each layer are inherited by an index hop link,which further reduces the loss of local feature information.Experiments show that the proposed network can achieve a classification accuracy of 92.78% during 42 min on the ModelNet40.Compared with the DGCNN(Dynamic Graph Convolutional Neural Network),the classification accuracy is improved by 0.58%.With the advantages of high classification accuracy and lightweight,this network model can be deployed in small embedded devices.
