  Point cloud analysis has drawn broader attentions due to its increasing
demands in various fields. Despite the impressive performance has been achieved
on several databases, researchers neglect the fact that the orientation of
those point cloud data is aligned. Varying the orientation of point cloud may
lead to the degradation of performance, restricting the capacity of
generalizing to real applications where the prior of orientation is often
unknown. In this paper, we propose the point projection feature, which is
invariant to the rotation of the input point cloud. A novel architecture is
designed to mine features of different levels. We adopt a PointNet-based
backbone to extract global feature for point cloud, and the graph aggregation
operation to perceive local shape structure. Besides, we introduce an efficient
key point descriptor to assign each point with different response and help
recognize the overall geometry. Mathematical analyses and experimental results
demonstrate that the proposed method can extract strictly rotation-invariant
representations for point cloud recognition and segmentation without data
augmentation, and outperforms other state-of-the-art methods.
