Revisiting Point Cloud Shape Classification with a Simple and Effective Baseline

Understand

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.