{"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/benchmarking-robustness-of-3d-point-cloud","title":"Benchmarking Robustness of 3D Point Cloud Recognition Against Common Corruptions","arxiv_id":"2201.12296","date":"2022-01-28","proceeding":null,"authors":["Jiachen Sun","Qingzhao Zhang","Bhavya Kailkhura","Zhiding Yu","Chaowei Xiao","Z. Morley Mao"],"abstract":"Deep neural networks on 3D point cloud data have been widely used in the real world, especially in safety-critical applications. However, their robustness against corruptions is less studied. 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