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