We propose a local-to-global representation learning algorithm for 3D point
cloud data, which is appropriate to handle various geometric transformations,
especially rotation, without explicit data augmentation with respect to the
transformations. Our model takes advantage of multi-level abstraction based on
graph convolutional neural networks, which constructs a descriptor hierarchy to
encode rotation-invariant shape information of an input object in a bottom-up
manner. The descriptors in each level are obtained from a neural network based
on a graph via stochastic sampling of 3D points, which is effective in making
the learned representations robust to the variations of input data. The
proposed algorithm presents the state-of-the-art performance on the
rotation-augmented 3D object recognition and segmentation benchmarks, and we
further analyze its characteristics through comprehensive ablative experiments.

本文提出了一种面对各种几何变换，特别是旋转的局部到全局表示学习算法，该算法利用基于图卷积神经网络的多级抽象构建描述符层次结构，以自底向上的方式对输入对象的旋转不变形状信息进行编码，并通过对三维点的随机采样有效地使学习到的表示对输入数据的变化具有鲁棒性。该算法在旋转增强的三维物体识别和分割基准测试上表现出最先进的性能。