  Despite the extensive usage of point clouds in 3D vision, relatively limited
data are available for training deep neural networks. Although data
augmentation is a standard approach to compensate for the scarcity of data, it
has been less explored in the point cloud literature. In this paper, we propose
a simple and effective augmentation method called PointWOLF for point cloud
augmentation. The proposed method produces smoothly varying non-rigid
deformations by locally weighted transformations centered at multiple anchor
points. The smooth deformations allow diverse and realistic augmentations.
Furthermore, in order to minimize the manual efforts to search the optimal
hyperparameters for augmentation, we present AugTune, which generates augmented
samples of desired difficulties producing targeted confidence scores. Our
experiments show our framework consistently improves the performance for both
shape classification and part segmentation tasks. Particularly, with
PointNet++, PointWOLF achieves the state-of-the-art 89.7 accuracy on shape
classification with the real-world ScanObjectNN dataset.
