  Point cloud datasets often suffer from inadequate sample sizes in comparison
to image datasets, making data augmentation challenging. While traditional
methods, like rigid transformations and scaling, have limited potential in
increasing dataset diversity due to their constraints on altering individual
sample shapes, we introduce the Biharmonic Augmentation (BA) method. BA is a
novel and efficient data augmentation technique that diversifies point cloud
data by imposing smooth non-rigid deformations on existing 3D structures. This
approach calculates biharmonic coordinates for the deformation function and
learns diverse deformation prototypes. Utilizing a CoefNet, our method predicts
coefficients to amalgamate these prototypes, ensuring comprehensive
deformation. Moreover, we present AdvTune, an advanced online augmentation
system that integrates adversarial training. This system synergistically
refines the CoefNet and the classification network, facilitating the automated
creation of adaptive shape deformations contingent on the learner status.
Comprehensive experimental analysis validates the superiority of Biharmonic
Augmentation, showcasing notable performance improvements over prevailing point
cloud augmentation techniques across varied network designs.
