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.
翻译:混合点云增殖是解决大规模公共数据集有限可用性问题的一个普遍办法。 但是,混合点和相应的语义标签之间的不匹配阻碍了在点性任务中进一步应用,例如部分分割。本文件建议采用点云增殖方法,即PointManifoldCut(PMC),取代神经网络嵌入点,而不是Euclidean空间坐标。这一方法的优势在于神经网络较高层次的点已经受过训练,可以嵌入其邻居关系,混合这些表达方式不会混淆自己和标签之间的关系。我们在PointManifoldCut操作后设置了一个空间变换模块,以对嵌入空间的新情况进行校正。本文也讨论了不同隐藏层的影响和替换点的方法。实验表明,我们拟议的方法可以提高点云分类以及分化网络的性能,并使它们对攻击和几何转换具有更大的活力。本文的代码见:https://github.com/fun0515/PointManfrifoldC。