为了进一步提高大规模多种类点云模型识别与分类的准确率,提出了一种K近邻卷积神经网络模型。首先,利用最远点采样算法对点云模型均匀采样;其次,对采样后的点云模型用K近邻算法构建每个点的局部邻域,为防止信息的非局部扩散,对卷积层提取的特征也逐个建立局部邻域;然后,通过最大池化聚合所有局部特征得到点云模型的全局特征表示;最后,用全连接层与Softmax函数计算各类别对应的概率并分类。实验结果表明,本算法在公开数据集ModelNet40上的识别准确率为92%。与已有的点云模型识别与分类算法相比,能更有效地融合局部结构特征,提高点云模型识别与分类的准确率。

In order to further improve the recognition and classification accuracy of large-scale multi-category point cloud model, a K-nearest neighbor convolutional neural network is proposed. First, the point cloud model is uniformly sampled with the farthest point sampling algorithm. Second, the K-nearest neighbor algorithm is used to construct the local neighborhood of each point for the sampled point cloud model. In order to prevent the non-local diffusion of information, a local neighborhood is constructed for each feature extracted from the convolution layer. Then, all local features are aggregated to obtain the global feature representation of the point cloud model through the max pooling. Finally, the probabilities corresponding to each category are calculated and classified using the fully connected layer and Softmax function. Experimental results show that the recognition accuracy of this algorithm on the ModelNet40 dataset is 92%. Compared with the current point cloud model recognition and classification algorithms, the proposed algorithm can more effectively fuse local structure features and improve the accuracy of point cloud model recognition and classification.