{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/pointaugment-an-auto-augmentation-framework","title":"PointAugment: an Auto-Augmentation Framework for Point Cloud Classification","arxiv_id":"2002.10876","date":"2020-02-25","proceeding":"CVPR 2020 6","authors":["Ruihui Li","Xianzhi Li","Pheng-Ann Heng","Chi-Wing Fu"],"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. 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