Feature line detection of noisy triangulated CSGbased objects using deep learning

Content Provider	Semantic Scholar
Author	Denk, Martin Paetzold, Kristin Rother, Klemens
Copyright Year	2019
Abstract	Feature lines such as sharp edges are the main characteristic lines of a surface. These lines are suitable as a basis for surface reconstruction and reverse engineering [1]. A supervised deep learning approach based on graph convolutional networks on estimating local feature lines will be introduced in the following. We test this deep learning architecture on two provided data sets of which one covers sharp feature lines and the other arbitrary feature lines based on unnoisy meshed constructive solid geometry [CSG]. Furthermore. we use a data balancing strategy by classifying different feature line types. We then compare the selected architecture with classical machine learning models. Finally. we show the detection of these lines on noisy and deformed meshes.
Starting Page	239
Ending Page	250
Page Count	12
File Format	PDF HTM / HTML
DOI	10.35199/dfx2019.21
Alternate Webpage(s)	https://www.designsociety.org/download-publication/42428/Feature+line+detection+of+noisy+triangulated+CSGbased+objects+using+deep+learning
Alternate Webpage(s)	https://doi.org/10.35199/dfx2019.21
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article