Knowledge Base Repository
In addition to research papers, the Design Society is developing several valuable resources for those interested in the study of design. These include a repository of PhD theses, a library of case studies and transcripts of design activities, and an archive of our newsletters. Please note that these resources are accessible exclusively to Design Society members.
Feature line detection of noisy triangulated CSGbased objects using deep learning
Martin Denk [1]; Kristin Paetzold [2]; Klemens Rother [1]
Type:
Year:
2019
Editor:
Dieter Krause; Kristin Paetzold; Sandro Wartzack
Author:
Series:
DfX
Institution:
Munich University of Applied Sciences [MUAS]; University of the German Federal Armed Forces Munich
Section:
Design for X
Page(s):
239-250
DOI number:
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.
Keywords:
