Web: http://arxiv.org/abs/2201.12296

Jan. 31, 2022, 2:11 a.m. | Jiachen Sun, Qingzhao Zhang, Bhavya Kailkhura, Zhiding Yu, Chaowei Xiao, Z. Morley Mao

cs.LG updates on arXiv.org arxiv.org

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. In this paper, we present ModelNet40-C,
the first comprehensive benchmark on 3D point cloud corruption robustness,
consisting of 15 common and realistic corruptions. Our evaluation shows a
significant gap between the performances on ModelNet40 and ModelNet40-C for
state-of-the-art (SOTA) models. To reduce the gap, we propose a simple but
effective method by …

3d arxiv benchmarking cloud

More from arxiv.org / cs.LG updates on arXiv.org

Data Scientist

@ Fluent, LLC | Boca Raton, Florida, United States

Big Data ETL Engineer

@ Binance.US | Vancouver

Data Scientist / Data Engineer

@ Kin + Carta | Chicago

Data Engineer

@ Craft | Warsaw, Masovian Voivodeship, Poland

Senior Manager, Data Analytics Audit

@ Affirm | Remote US

Data Scientist - Nationwide Opportunities, AWS Professional Services

@ Amazon.com | US, NC, Virtual Location - N Carolina