Weaknesses: The idea of estimating and relying on local reference frame to achieve rotation invariance has been explored before in similar context, thus might downgrade the novelty of this paper. For example, "A-CNN: Annularly Convolutional Neural Networks on Point Clouds, CVPR'19" uses the local point set to estimate the normal as this paper does, the difference is that A-CNN uses this normal to project 3d points into 2d plane, however, the basic idea of them is both to achieve locally rotation invariance. "Relation-Shape Convolutional Neural Network for Point Cloud Analysis, CVPR'19" mentioned in their experiments about rotation invariance that they construct a local reference frame to achieve rotation invariant representation of local point set which is the same as this paper. The randomized technique is also a common technique in training deep networks for exploring a larger data space or parameter space. The whole hierarchy is identical to PointNet .

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.