Feature line detection of noisy triangulated CSGbased objects using deep learning
Denk M, Paetzold K, Rother K (2019)
Publication Type: Conference contribution
Publication year: 2019
Original Authors: Martin Denk, , Kristin Paetzold, Klemens Rother,
Pages Range: 239 - 250
DOI: 10.35199/dfx2019.21
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.
Authors with CRIS profile
Involved external institutions
Hochschule für angewandte Wissenschaften München
Germany (DE)
Universität der Bundeswehr München
Germany (DE)
Germany (DE)
Universität der Bundeswehr München
Germany (DE)
How to cite
APA:
Denk, M., Paetzold, K., & Rother, K. (2019). Feature line detection of noisy triangulated CSGbased objects using deep learning. In Proceedings of the DS 98: Proceedings of the 30th Symposium Design for X (DFX 2019) (pp. 239 - 250).
MLA:
Denk, Martin, Kristin Paetzold, and Klemens Rother. "Feature line detection of noisy triangulated CSGbased objects using deep learning." Proceedings of the DS 98: Proceedings of the 30th Symposium Design for X (DFX 2019) 2019. 239 - 250.
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