A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf
.
Scalability of Learning Tasks on 3D CAE Models Using Point Cloud Autoencoders
2019
2019 IEEE Symposium Series on Computational Intelligence (SSCI)
Geometric Deep Learning (GDL) methods have recently gained interest as powerful, high-dimensional models for approaching various geometry processing tasks. However, training deep neural network models on geometric input requires considerable computational effort, even more so if one considers typical problem sizes found in application domains such as engineering tasks, where geometric data are often orders of magnitude larger than the inputs currently considered in GDL literature. Hence, an
doi:10.1109/ssci44817.2019.9002982
dblp:conf/ssci/RiosWSBXSM19
fatcat:pa7xufpfsbcalijlyzq5y5c76y