FPSMix: data augmentation strategy for point cloud classification

Abstract

Data augmentation is a widely used regularization strategy in deep neural networks to mitigate overfitting and enhance generalization. In the context of point cloud data, mixing two samples to generate new training examples has proven to be effective. In this paper, we propose a novel and effective approach called Farthest Point Sampling Mix (FPSMix) for augmenting point cloud data. Our method leverages farthest point sampling, a technique used in point cloud processing, to generate new samples by mixing points from two original point clouds. Another key innovation of our approach is the introduction of a significance-based loss function, which assigns weights to the soft labels of the mixed samples based on the classification loss of each part of the new sample that is separated from the two original point clouds. This way, our method takes into account the importance of different parts of the mixed sample during the training process, allowing the model to learn better global features. Experimental results demonstrate that our FPSMix, combined with the significance-based loss function, improves the classification accuracy of point cloud models and achieves comparable performance with state-of-the-art data augmentation methods. Moreover, our approach is complementary to techniques that focus on local features, and their combined use further enhances the classification accuracy of the baseline model.