PointAugment: an Auto-Augmentation Framework for Point Cloud Classification
This addresses data scarcity issues in 3D vision tasks like point cloud classification, offering a domain-specific solution for researchers and practitioners in computer vision.
The authors tackled the problem of limited data diversity in point cloud classification by introducing PointAugment, an auto-augmentation framework that uses adversarial learning to optimize augmentations, resulting in improved performance on shape classification and retrieval tasks.
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.