CEDR: Contrastive Embedding Distribution Refinement for 3D point cloud representation

The distinguishable deep features are essential for the 3D point cloud recognition as they influence the search for the optimal classifier. Most existing point cloud classification methods mainly focus on local information aggregation while ignoring the feature distribution of the whole dataset that indicates more informative and intrinsic semantic relationships of labeled data, if better exploited, which could learn more distinguishing inter-class features. Our work attempts to construct a more distinguishable feature space through performing feature distribution refinement inspired by contrastive learning and sample mining strategies, without modifying the model architecture. To explore the full potential of feature distribution refinement, two modules are involved to boost exceptionally distributed samples distinguishability in an adaptive manner: (i) Confusion-Prone Classes Mining (CPCM) module is aimed at hard-to-distinct classes, which alleviates the massive category-level confusion by generating class-level soft labels; (ii) Entropy-Aware Attention (EAA) mechanism is proposed to remove influence of the trivial cases which could substantially weaken model performance. Our method achieves competitive results on multiple applications of point cloud. In particular, our method gets 85.8% accuracy on ScanObjectNN, and substantial performance gains up to 2.7% in DCGNN, 3.1% in PointNet++, and 2.4% in GBNet. Our code is available at https://github.com/YangFengSEU/CEDR.