PointMixup: Data Augmentation for Point Clouds

Yunlu Chen
Vincent Tao Hu
Efstratios Gavves
Pascal Mettes
Pengwan Yang
Cees Snoek


This paper introduces a data augmentation for point clouds by interpolation between examples. Data augmentation by interpolation has shown to be a simple and effective approach in the image domain. Such a mixup is however not directly transferable to point clouds, as we do not have a one-to-one correspondence between the points of two different objects. In this paper, we introduce optional assignment mixup to enable data augmentation by interpolation of point clouds. Our proposed mixup generates new point cloud examples as a linear interpolation of the shortest path between two point clouds. This shortest path is given by the optimal bijection as specified with the Earth Mover's Distance. We prove that our optimal assignment mixup abides to the shortest path property, linearity of the interpolation, and assignment invariance. Experimentally, we show the potential of the mixup for point cloud classification, especially when examples are scarce.

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