No Pain, Big Gain: Classify Dynamic Point Cloud Sequences with Static Models by Fitting Feature-level Space-time Surfaces

MLA

Zhong, Jia-Xing, et al. No Pain, Big Gain: Classify Dynamic Point Cloud Sequences with Static Models by Fitting Feature-Level Space-Time Surfaces. 2022. EBSCOhost, widgets.ebscohost.com/prod/customlink/proxify/proxify.php?count=1&encode=0&proxy=&find_1=&replace_1=&target=https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&scope=site&db=edsarx&AN=edsarx.2203.11113&authtype=sso&custid=ns315887.

APA

Zhong, J.-X., Zhou, K., Hu, Q., Wang, B., Trigoni, N., & Markham, A. (2022). No Pain, Big Gain: Classify Dynamic Point Cloud Sequences with Static Models by Fitting Feature-level Space-time Surfaces.

Chicago

Zhong, Jia-Xing, Kaichen Zhou, Qingyong Hu, Bing Wang, Niki Trigoni, and Andrew Markham. 2022. “No Pain, Big Gain: Classify Dynamic Point Cloud Sequences with Static Models by Fitting Feature-Level Space-Time Surfaces.” http://widgets.ebscohost.com/prod/customlink/proxify/proxify.php?count=1&encode=0&proxy=&find_1=&replace_1=&target=https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&scope=site&db=edsarx&AN=edsarx.2203.11113&authtype=sso&custid=ns315887.