3D object classification from point clouds

Artificial intelligence has achieved a breakthrough with the proposal and development of deep learning. Compared with traditional models, deep learning allows machines to extract features and train neural networks by learning weight parameters. Convolutional Neural Networks (CNN), as the top priority of deep learning, have achieved remarkable results in 2D image recognition and classification segmentation. Recently, points cloud is a recent hot 3D data form in the field of deep learning. Point clouds retain better spatial geometric information than other forms of 3D data such as mesh depth maps. Due to the disorder, rotation invariance, the uneven density distribution of 3D point clouds, high sensor noise, and complex scenes, deep learning of 3D point clouds is still in the initial stage, and there are significant challenges. The tasks of deep learning for point clouds are mainly classified into shape classification, instance segmentation, semantic segmentation, etc. This article specifically outlines the development of methods for shape classification tasks and the characteristics and differences of each method. In addition, a comparison of the training accuracy and efficiency of each method on the dataset is provided.