Revisit Point Cloud Recognition From the Viewpoint of Data Mixture

In recent years, point cloud based data analysis has attracted lots of attentions of researchers from many different fields because of its simplicity and effectiveness. As a fundamental research, point cloud representation and recognition plays an important role. Although existing works achieved some good performances, they still cannot take full use of the information hidden in training data. This paper revisits the problem of point cloud representation and recognition from the viewpoint of data augmentation without incorporating additional data or more annotations. Different from existing works on 1D or 2D data, our proposed approach deals with a more complicated problem in three dimensional space for point cloud representation and recognition by mixing various training data from different object categories, which could help the classifier to better optimize the data-driven parameters. To validate the performance of our proposed approach, the popular used ModelNet40 dataset is employed as the standard benchmark. By carrying out comprehensive experiments under many different conditions, the experimental results show that our mixture method works positively towards improving the recognition performance of point cloud.