  Data augmentation is an effective regularization strategy to alleviate the
overfitting, which is an inherent drawback of the deep neural networks.
However, data augmentation is rarely considered for point cloud processing
despite many studies proposing various augmentation methods for image data.
Actually, regularization is essential for point clouds since lack of generality
is more likely to occur in point cloud due to small datasets. This paper
proposes a Rigid Subset Mix (RSMix), a novel data augmentation method for point
clouds that generates a virtual mixed sample by replacing part of the sample
with shape-preserved subsets from another sample. RSMix preserves structural
information of the point cloud sample by extracting subsets from each sample
without deformation using a neighboring function. The neighboring function was
carefully designed considering unique properties of point cloud, unordered
structure and non-grid. Experiments verified that RSMix successfully
regularized the deep neural networks with remarkable improvement for shape
classification. We also analyzed various combinations of data augmentations
including RSMix with single and multi-view evaluations, based on abundant
ablation studies.
