Google Research

PointMixup: Data Augmentation for Point Clouds

  • Yunlu Chen
  • Vincent Tao Hu
  • Efstratios Gavves
  • Thomas Mensink
  • Pascal Mettes
  • Pengwan Yang
  • Cees Snoek
ECCV (2020)

Abstract

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.

Research Areas

Learn more about how we do research

We maintain a portfolio of research projects, providing individuals and teams the freedom to emphasize specific types of work