  Learning a powerful representation from point clouds is a fundamental and
challenging problem in the field of computer vision. Different from images
where RGB pixels are stored in the regular grid, for point clouds, the
underlying semantic and structural information of point clouds is the spatial
layout of the points. Moreover, the properties of challenging in-context and
background noise pose more challenges to point cloud analysis. One assumption
is that the poor performance of the classification model can be attributed to
the indistinguishable embedding feature that impedes the search for the optimal
classifier. This work offers a new strategy for learning powerful
representations via a contrastive learning approach that can be embedded into
any point cloud classification network. First, we propose a supervised
contrastive classification method to implement embedding feature distribution
refinement by improving the intra-class compactness and inter-class
separability. Second, to solve the confusion problem caused by small
inter-class compactness and inter-class separability. Second, to solve the
confusion problem caused by small inter-class variations between some
similar-looking categories, we propose a confusion-prone class mining strategy
to alleviate the confusion effect. Finally, considering that outliers of the
sample clusters in the embedding space may cause performance degradation, we
design an entropy-aware attention module with information entropy theory to
identify the outlier cases and the unstable samples by measuring the
uncertainty of predicted probability. The results of extensive experiments
demonstrate that our method outperforms the state-of-the-art approaches by
achieving 82.9% accuracy on the real-world ScanObjectNN dataset and substantial
performance gains up to 2.9% in DCGNN, 3.1% in PointNet++, and 2.4% in GBNet.
