Deep learning on point clouds has made a lot of progress recently. Many point
cloud dedicated deep learning frameworks, such as PointNet and PointNet++, have
shown advantages in accuracy and speed comparing to those using traditional 3D
convolution algorithms. However, nearly all of these methods face a challenge,
since the coordinates of the point cloud are decided by the coordinate system,
they cannot handle the problem of 3D transform invariance properly. In this
paper, we propose a general framework for point cloud learning. We achieve
transform invariance by learning inner 3D geometry feature based on local graph
representation, and propose a feature extraction network based on graph
convolution network. Through experiments on classification and segmentation
tasks, our method achieves state-of-the-art performance in rotated 3D object
classification, and achieve competitive performance with the state-of-the-art
in classification and segmentation tasks with fixed coordinate value.

本论文提出了一种基于局部图表示的点云学习框架，通过学习内部的三维几何特征并使用基于图卷积网络的特征提取网络来实现三维变换不变性，这种方法在分类和分割任务中取得了最先进的性能和有竞争力的性能，具有重要的研究意义。