针对PointNet模型只针对孤立点提取特征信息,而对邻域结构的信息提取能力不足的问题,提出基于图卷积网络的三维点云分类分割模型GraphPNet。首先将三维点云转换成无向图结构,利用该图结构得到点云的邻域信息,通过将邻域信息与单个点信息融合的方式提高分类与分割的准确率。在分类实验中,本文在ModelNet40数据集上进行训练与测试,并且与3D ShapeNets、VoxNet、PointNet模型的分类精度进行比较,其分类精度优于这些模型。在分割实验中,使用ShapeNet数据集进行训练与测试,并且与PointNet模型等分割模型得到的平均交并比(mIoU)值进行比较,验证了GraphPNet在分割实验中的有效性。

PointNet model only extracts features of isolated points and therefore does not consider neighborhood structure information among points. To address this limitation, we propose GraphPNet, a 3D point cloud classification and segmentation model based on graph convolutional networks. The 3D point cloud is transformed into an undirected graph structure. Then, the neighborhood structure information of the 3D point cloud is obtained from the undirected graph structure. Classification and segmentation accuracy are improved by fusing neighborhood information with single point information. In classification experiments, GraphPNet is trained and tested on the ModelNet40 dataset and compared with VoxNet, PointNet, and 3D ShapeNets models. The results demonstrate that GraphPNet obtains better accuracy than the other models. In segmentation experiments, the ShapeNet dataset is used for training and testing, and the mean intersection over union values of GraphPNet and other segmentation models, such as PointNet, are compared. The results confirm the effectiveness of the proposed GraphPNet model.