  Data augmentation is an effective regularization strategy for mitigating
overfitting in deep neural networks, and it plays a crucial role in 3D vision
tasks, where the point cloud data is relatively limited. While mixing-based
augmentation has shown promise for point clouds, previous methods mix point
clouds either on block level or point level, which has constrained their
ability to strike a balance between generating diverse training samples and
preserving the local characteristics of point clouds. Additionally, the varying
importance of each part of the point clouds has not been fully considered,
cause not all parts contribute equally to the classification task, and some
parts may contain unimportant or redundant information. To overcome these
challenges, we propose PointPatchMix, a novel approach that mixes point clouds
at the patch level and integrates a patch scoring module to generate
content-based targets for mixed point clouds. Our approach preserves local
features at the patch level, while the patch scoring module assigns targets
based on the content-based significance score from a pre-trained teacher model.
We evaluate PointPatchMix on two benchmark datasets, ModelNet40 and
ScanObjectNN, and demonstrate significant improvements over various baselines
in both synthetic and real-world datasets, as well as few-shot settings. With
Point-MAE as our baseline, our model surpasses previous methods by a
significant margin, achieving 86.3% accuracy on ScanObjectNN and 94.1% accuracy
on ModelNet40. Furthermore, our approach shows strong generalization across
multiple architectures and enhances the robustness of the baseline model.
