Revisiting Point Cloud Shape Classification with a Simple and Effective\n Baseline

Processing point cloud data is an important component of many real-world\nsystems. As such, a wide variety of point-based approaches have been proposed,\nreporting steady benchmark improvements over time. We study the key ingredients\nof this progress and uncover two critical results. First, we find that\nauxiliary factors like different evaluation schemes, data augmentation\nstrategies, and loss functions, which are independent of the model\narchitecture, make a large difference in performance. The differences are large\nenough that they obscure the effect of architecture. When these factors are\ncontrolled for, PointNet++, a relatively older network, performs competitively\nwith recent methods. Second, a very simple projection-based method, which we\nrefer to as SimpleView, performs surprisingly well. It achieves on par or\nbetter results than sophisticated state-of-the-art methods on ModelNet40 while\nbeing half the size of PointNet++. It also outperforms state-of-the-art methods\non ScanObjectNN, a real-world point cloud benchmark, and demonstrates better\ncross-dataset generalization. Code is available at\nhttps://github.com/princeton-vl/SimpleView.\n