索引边缘几何卷积神经网络用于点云分类

Index edge geometric convolution neural network for point cloud classification

点云学习因其在计算机视觉、自动驾驶和机器人技术等领域的广泛应用而受到越来越多的关注。点云数据具有稀疏性、无序性、有限性等特点,给基于深度卷积神经网络的点云分类任务带来很大困难,通常采用多视图或将点云转换为体素后使用卷积神经网络进行处理,但转换过程会带来局部特征信息丢失、计算效率低等问题。将原始点云直接输入到分类网络还存在参数量过多、网络规模复杂等问题,实时性任务处理仍需进一步优化。为使点云处理网络轻量化,以适用于实时点云分类任务,提出索引边缘几何卷积神经网络模型。首先对网络结构和超参进行裁剪并压缩,实现模型轻量化;其次,使用k近邻算法在每个卷积层上确定新的局部区域,加入相邻点之间的向量方向,将不同层的输出特征映射并进行索引跳跃连接,使局部特征信息损失进一步降低。在ModelNet40数据集上,该方法的分类准确率约为92.78%,与DGCNN相比,提高了约0.58%。实验结果表明,所提出的模型具有分类准确率高、轻量化特点,可部署于小型嵌入式设备。

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.