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Machine learning methods for reverse engineering of defective structured surfaces

  • Pascal Laube presents machine learning approaches for three key problems of reverse engineering of defective structured surfaces: parametrization of curves and surfaces, geometric primitive classification and inpainting of high-resolution textures. The proposed methods aim to improve the reconstruction quality while further automating the process. The contributions demonstrate that machine learning can be a viable part of the CAD reverse engineering pipeline.

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Metadaten
Author:Pascal Laube
URL:https://link.springer.com/book/10.1007/978-3-658-29017-7
DOI:https://doi.org/10.1007/978-3-658-29017-7
ISBN:978-3-658-29017-7
ISBN:978-3-658-29016-0
Parent Title (German):Schriftenreihe der Institute für Systemdynamik (ISD) und optische Systeme (IOS)
Publisher:Springer Vieweg
Place of publication:Wiesbaden
Advisor:Georg Umlauf, Oliver Deussen
Document Type:Doctoral Thesis
Language:English
Year of Publication:2020
Granting Institution:Universität Konstanz
Date of final exam:2019/07/25
Release Date:2020/01/21
Pagenumber:160
Institutes:Institut für Optische Systeme - IOS
Relevance:Abgeschlossene Dissertation
Open Access?:Nein
Licence (English):License LogoLizenzbedingungen Springer