PointAugment: An Auto-Augmentation Framework for Point Cloud Classification
Abstract
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 retrival.
Cite
Text
Li et al. "PointAugment: An Auto-Augmentation Framework for Point Cloud Classification." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020. doi:10.1109/CVPR42600.2020.00641Markdown
[Li et al. "PointAugment: An Auto-Augmentation Framework for Point Cloud Classification." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020.](https://mlanthology.org/cvpr/2020/li2020cvpr-pointaugment/) doi:10.1109/CVPR42600.2020.00641BibTeX
@inproceedings{li2020cvpr-pointaugment,
title = {{PointAugment: An Auto-Augmentation Framework for Point Cloud Classification}},
author = {Li, Ruihui and Li, Xianzhi and Heng, Pheng-Ann and Fu, Chi-Wing},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2020},
doi = {10.1109/CVPR42600.2020.00641},
url = {https://mlanthology.org/cvpr/2020/li2020cvpr-pointaugment/}
}