论文标题
点：点云分类的自动提升框架
PointAugment: an Auto-Augmentation Framework for Point Cloud Classification
论文作者
论文摘要
我们介绍了一个新的自动提升框架，该框架将自动优化和增强点云样本，以在我们培训分类网络时丰富数据多样性。与现有的2D图像的自动启发方法不同，点声是样本感知的，并采用对抗性学习策略来共同优化增强器网络和分类器网络，因此增强器可以学会生成最适合分类器的增强样品。此外，我们通过形状的转换和点位移来制定可学习的点增强功能，并仔细设计损失功能，以根据分类器的学习进度来采用增强样本。广泛的实验还证实了点淘汰的有效性和鲁棒性，以提高各种网络在形状分类和检索方面的性能。
We present PointAugment, a new auto-augmentation framework that automatically optimizes and augments point cloud samples to enrich the data diversity when we train a classification network. Different from existing auto-augmentation methods for 2D images, PointAugment is sample-aware and takes an adversarial learning strategy to jointly optimize an augmentor network and a classifier network, such that the augmentor can learn to produce augmented samples that best fit the classifier. Moreover, we formulate a learnable point augmentation function with a shape-wise transformation and a point-wise displacement, and carefully design loss functions to adopt the augmented samples based on the learning progress of the classifier. Extensive experiments also confirm PointAugment's effectiveness and robustness to improve the performance of various networks on shape classification and retrieval.
