  We present PointAugment, a new auto-augmentation framework that automatically
optimizes and augments point cloud samples to enrich the data diversity when we
train a classification network. Different from existing auto-augmentation
methods for 2D images, PointAugment is sample-aware and takes an adversarial
learning strategy to jointly optimize an augmentor network and a classifier
network, such that the augmentor can learn to produce augmented samples that
best fit the classifier. Moreover, we formulate a learnable point augmentation
function with a shape-wise transformation and a point-wise displacement, and
carefully design loss functions to adopt the augmented samples based on the
learning progress of the classifier. Extensive experiments also confirm
PointAugment's effectiveness and robustness to improve the performance of
various networks on shape classification and retrieval.
