基于自注意力机制的点云分类分割

Point Cloud Classification and Segmentation Based on Self-attention Mechanism

3D点云由于其无序性以及缺少拓扑信息使得点云的分类与分割仍具有挑战性.针对上述问题,我们设计了一种基于自注意力机制的3D点云分类算法,可学习点云的特征信息,用于目标分类与分割.首先,设计适用于点云的自注意力模块,用于点云的特征提取.通过构建领域图来加强输入嵌入,使用自注意力机制进行局部特征的提取与聚合.最后,通过多层感知机以及解码器-编码器的方式将局部特征进行结合,实现3D点云的分类与分割.该方法考虑了输入嵌入时单个点在点云中的局部语境信息,构建局部长距离下的网络结构,最终得到的结果更具区分度.在ShapeNetPart、RoofN3D等数据集上的实验证实所提方法的分类与分割性能较优.

Due to the disorder and lack of topological information,the classification and segmentation of 3D point clouds is still challenging.To this end,this study designs a 3D point cloud classification algorithm based on the self-attention mechanism to learn point cloud feature information for object classification and segmentation.Firstly,a self-attention module suitable for point clouds is designed for feature extraction.A neighborhood graph is constructed to enhance the input embedding,and the local features are extracted and aggregated by utilizing the self-attention mechanism.Finally,the local features are combined via multi-layer perceptron and encoder-decoder approaches to achieve 3D point cloud classification and segmentation.This method considers the local context information of individual points in the point cloud during input embedding,constructs a network structure under local long distances,and ultimately yields more distinctive results.Experiments on datasets such as ShapeNetPart and RoofN3D demonstrate that the proposed method performs better in classification and segmentation.