BÜCHER VERSANDKOSTENFREI INNERHALB DEUTSCHLANDS
eBook
Machine Learning Methods for Reverse Engineering of Defective Structured Surfaces
Springer Gabler
ISBN 978-3-658-29017-7
Standardpreis
Bibliografische Daten
eBook. PDF. Weiches DRM (Wasserzeichen)
2020
XV, 161 p. 56 illus..
In englischer Sprache
Umfang: 161 S.
Verlag: Springer Gabler
ISBN: 978-3-658-29017-7
Weiterführende bibliografische Daten
Das Werk ist Teil der Reihe: Schriftenreihe der Institute für Systemdynamik (ISD) und optische Systeme (IOS)
Produktbeschreibung
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.
Contents
- Machine Learning Methods for Parametrization in Curve and Surface Approximation
- Classification of Geometric Primitives in Point Clouds
- Image Inpainting for High-resolution Textures Using CNN Texture Synthesis
Target Groups
- Lecturers and students in the field of machine learning, geometric modeling and information theory
- Practitioners in the field of machine learning, surface reconstruction and CAD
The Author
Pascal Laube's main research interest is the development of machine learning methods for CAD reverse engineering. He is currently developing self-driving cars for an international operating German enterprise in the field of mobility, automotive and industrial technology.
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