  Point cloud analysis is challenging due to the irregularity of the point
cloud data structure. Existing works typically employ the ad-hoc
sampling-grouping operation of PointNet++, followed by sophisticated local
and/or global feature extractors for leveraging the 3D geometry of the point
cloud. Unfortunately, the sampling-grouping operations do not address the point
cloud's irregularity, whereas the intricate local and/or global feature
extractors led to poor computational efficiency. In this paper, we introduce a
novel DualNorm module after the sampling-grouping operation to effectively and
efficiently address the irregularity issue. The DualNorm module consists of
Point Normalization, which normalizes the grouped points to the sampled points,
and Reverse Point Normalization, which normalizes the sampled points to the
grouped points. The proposed framework, PointNorm, utilizes local mean and
global standard deviation to benefit from both local and global features while
maintaining a faithful inference speed. Experiments show that we achieved
excellent accuracy and efficiency on ModelNet40 classification, ScanObjectNN
classification, ShapeNetPart Part Segmentation, and S3DIS Semantic
Segmentation. Code is available at
https://github.com/ShenZheng2000/PointNorm-for-Point-Cloud-Analysis.
