Research on Point Cloud Classification and Its Robustness Based on Self-Distillation Framework
Compared to that of a Two-Dimensional(2D)image dataset,a Three-Dimensional(3D)point cloud dataset is smaller in scale and poorly represented,which easily leads to problems of overfitting and poor generalization ability of neural networks.Accordingly,a Point cloud Self-Distillation(PointSD)framework is proposed.This framework enables the network to extract more feature information from the original point cloud data by learning data samples with different representation forms,thus realizing the knowledge interaction between samples,improving the generalization capabilities of the network without increasing the additional computational load,and making the network suitable for classification network models of different scales.Based on this framework,a point cloud anti-corruption training method,TND-PointSD,is proposed,which solves the problem of the insufficient anti-corruption capabilities of the current point cloud training methods.Experimental results show that the Mean Accuracy(MA)of the PointNet++and RepSurf-U‡2X benchmark networks using the PointSD framework are 8.22 and 4.86 percentage points higher respectively,than those of the Standard Training(ST)method on the ScanObjectNN dataset.In addition,the Mean Overall Accuracy(MOA)of the classification networks on the ModelNet40-C dataset is improved for 15 corruption types.The study thus shows that the TND-PointSD method can effectively enhance the corruption robustness of the network model.
point cloud datapoint cloud classificationself-distillationdata enhancementcorruption robustness
万方数据
维普
