PCSalmix: Gradient Saliency-Based Mix Augmentation for Point Cloud Classification

Point cloud classification has sparked many researchers’ interest for its cornerstone role in 3D applications. Inheriting the CutMix series augmentation that performs well in 2D images, PointCutMix and RSMix are proposed to generate new samples for 3D point clouds, by replacing partial points of one cloud with those of another. However, the selection of mixed regions is all built on randomness, ignoring the significance of point clouds’ saliency. To address this deficiency, we propose PCSalMix: a novel Saliency-based Mix augmentation for Point Cloud classification. The gradient of classification network on inputs is a natural tool to locate the saliency. Based on this discovery, we extract points with larger gradient values to make more representative samples. Afterward, the soft labels are weighted more accurately by accumulated gradients rather than count ratios of points. The experimental results verify the outperformance of our method on ModelNet40 and ModelNet10 benchmarks in terms of accuracy and robustness against adversarial attacks.