HD-PointNet: point cloud processing in higher dimensions

Abstract

PointNet++ is a simple but effective network designed for point cloud processing. However, the accuracy of PointNet++ has been surpassed by many other methods, like DGCNN and Point Cloud Transformer. These methods are way heavier compared to PointNet++, which is not favorable for the deployment of real-world products. In this paper, we propose a module called HD projection layers that was inspired by nonlinear kernels used in support vector machines. The HD projection layers project the features of the point cloud into a higher dimension, increasing the linear separability and therefore relieving the burden on the classifier. Equipped with HD projection layers, we extended PointNet++ into a new network, HD-PointNet, which also involves many other improvements and better training techniques. Experiments show that the accuracy of HD-PointNet is competitive against other modern methods while using fewer computation resources.