Large Scale Point Cloud Classification Base on Graph-MLP++

Deep learning has made remarkable achievements in the object classification of 2D images. However, the 3D point cloud classification task is still an open challenge due to the point cloud being irregular and with a mass of noise. This work proposed a large-scale point cloud processing framework that can improve the accuracy and efficiency of point cloud classification. The proposed method calculates the feature values of the point cloud to construct the point cloud feature images, then inputs them into the Graph-MLP++ network to get the point cloud classification result. GraphMLP++ can achieve 97.8% accuracy in the Oakland dataset. Compared with other methods, the efficiency and accuracy of the result are competitive