Point Cloud Classification Segmentation Combining Inter-Region Structure Relations and Self-Attention Edge Convolution Network

A new network framework, ISEC-Net (inter-region structure relations and self-attention edge convolution network), is proposed to address the problem of insufficient capture of context and relational features within a region in point cloud networks for deep learning. The network consists of two modules:IrConv (inter-region convolution) and SaConv (self-attention convolution). The SaConv module can extract finer edge features, while the IrConv can dynamically integrate local structural information into point features and adaptively capture inter-regional relationships. Extensive experiments are conducted on the ModelNet40 and ShapeNet datasets for point cloud classification and part segmentation. The results show that on the ModelNet40 dataset, the overall accuracy (OA) of the ISEC-Net model reaches 93.5%, and the average accuracy (mAcc) reaches 90.7%. On the ShapeNet dataset, the average intersection-over-union (mIoU) reaches 86.1%, and the part segmentation accuracy of guitar, headphone, cup and other parts in the single-class intersection-over-union (IoU) experiment is excellent. This demonstrates that compared with traditional dynamic graph convolutional networks, ISEC-Net can accurately capture the local features and fine structure of point clouds and enhance the aggregation of global features, thus having excellent effectiveness and generalization ability. © 2024 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.