基于KNN-PointNet的点云语义分割

Point Cloud Semantic Segmentation Based on KNN-PointNet

针对深度神经网络PointNet未引入局部特征以及分割精度有待提高的问题,在PointNet的基础上提出一种结合改进K近邻(KNN)算法的局部特征提取方法,将引入局部特征提取方法的神经网络命名为KNNPointNet。首先将局部区域划分为k个圆形邻域,根据局部区域中样本数据分布密度的差异来确定权值以计算待测点的分类情况;其次将局部邻域特征结合单点全局特征作为输入进行特征提取,通过调节网络深度来提取局部特征以增强局部邻域中点与点的相互关联;最后将改进的KNN算法应用于KNN-PointNet点云分割网络进行实验对比。实验结果表明,相比于当前一些先进的分割网络,采用改进KNN算法的分割网络KNN-PointNet具有更高的分割精度。

To overcome the lack of local features of the deep neural network PointNet and the need for the improvement of segmentation accuracy, the present research introduces a local feature extraction method combined with an improved K-nearest neighbor(KNN) algorithm based on PointNet and a neural network known as KNN-PointNet. First, the local area is divided into k circular neighborhoods, and weights are determined according to the difference in the distribution density of sample data in the local area to calculate the classification of the points to be measured. Second, the local neighborhood features combined with single point global features are used as input for feature extraction by adjusting the network depth to extract local features for enhancing the correlation between points in the local neighborhood. Finally, the improved KNN algorithm is applied to the KNN-PointNet point cloud segmentation network for experimental comparison. Results show that compared with some current advanced segmentation networks, the segmentation network KNN-PointNet with local features extracted by the improved KNN algorithm has higher segmentation accuracy.