Research on Lightweight Point Cloud Classification Based on Deformable 3D Graph Convolution
Existing deep learning methods rely on absolute point coordinates when addressing point cloud classification tasks,which encounter the large model complexity problem.To address this challenge,a lightweight point cloud classification network called DMGCN-3D is proposed herein.The adaptive hollow K-Nearest Neighbor(KNN)algorithm is used to construct the graph structure,capture geometric structure information regarding the local wider space,and reduce calculation costs.A deformable 3-Dimensional(3D)graph convolution is constructed,and the learnable direction vector between points is introduced to obtain relative characteristics between points.The displacement and scale invariances of point clouds are guaranteed during the feature extraction process.A multi-head self-attention module is constructed,and the residual structure is combined with Group Shift Attention(GSA)and the Multi-Layer Perceptron(MLP)network.The MLP assists in maintaining the integrity of original point cloud information,and the GSA enables the network to learn the internal autocorrelation of features,which improves feature expression capability and reduces the total number of model parameters.A spatial transformation network combined with the MLP is used to learn point cloud features.Finally,the extracted features are fused to obtain more comprehensive point cloud classification features.The experimental results demonstrate that the overall accuracies of DMGCN-3D on ModelNet10,ModelNet40,and ScanObjectNN are 96.5％,94.7％,and 81.9％,respectively,which is 2.9,2.1,and 3.8 percentage points higher than those of the DGCNN.Compared with DGCNN,LDGCNN,and 3DGCN,the total number of parameters is reduced by 52.9％,23.9％,and 3.3％,respectively.Additionally,high robustness is maintained,which demonstrates an improvement on that of existing advanced methods.
point cloud classificationdeformable 3D graph convolutionself-adaptionmultiple head self-attentionlightweight network
万方数据
维普
