PointManifoldCut: Point-wise Augmentation in the Manifold for Point Clouds

TL;DR

PointManifoldCut introduces a novel point cloud augmentation method that replaces embedded points within neural networks, improving classification, segmentation performance, and robustness against attacks and transformations.

Contribution

This paper proposes PointManifoldCut, a new augmentation technique that replaces neural network embedded points rather than raw coordinates, enhancing point cloud task performance.

Findings

Improves point cloud classification accuracy.

Enhances segmentation network performance.

Increases robustness to attacks and geometric transformations.

Abstract

Mixed-based point cloud augmentation is a popular solution to the problem of limited availability of large-scale public datasets. But the mismatch between mixed points and corresponding semantic labels hinders the further application in point-wise tasks such as part segmentation. This paper proposes a point cloud augmentation approach, PointManifoldCut(PMC), which replaces the neural network embedded points, rather than the Euclidean space coordinates. This approach takes the advantage that points at the higher levels of the neural network are already trained to embed its neighbors relations and mixing these representation will not mingle the relation between itself and its label. We set up a spatial transform module after PointManifoldCut operation to align the new instances in the embedded space. The effects of different hidden layers and methods of replacing points are also discussed in this paper. The experiments show that our proposed approach can enhance the performance of point cloud classification as well as segmentation networks, and brings them additional robustness to attacks and geometric transformations. The code of this paper is available at: https://github.com/fun0515/PointManifoldCut.