Research on 3D Point Cloud Classification Algorithm by Fusion of PCT and PointMLP
In order to improve the classification accuracy of 3D point cloud data,the fusion algorithm of PCT with attention mechanism and PointMLP with residual point MLP is investigated.The improved algo-rithm firstly adds a geometric affine module to normalize the input point cloud to solve the density inhomo-geneity and geometrical uncertainty of the point cloud;then the coding module firstly uses the attention mechanism to globally express the upper layer features,and then adds a residual point MLP module to re-present the depth features of the output features of the attention module to fully extract the point cloud fea-tures;and finally,the classification module to complete the downstream point cloud classification task.The experimental results show that the improved algorithm achieves a classification accuracy of 95.6%on the ModelNet40 dataset,which is a 2.4%increase in accuracy compared to the PCT model,and is robust to changes in the number of point clouds.
3D point cloudattention mechanismresidual point MLPpoint cloud classification
万方数据
维普
