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Machine Learning Methods for Reverse Engineering of Defective Structured Surfaces de Pascal Laube

Machine Learning Methods for Reverse Engineering of Defective Structured Surfaces: Schriftenreihe der Institute für Systemdynamik (ISD) und optische Systeme (IOS)

Autor Pascal Laube
en Limba Engleză Paperback – 3 ian 2020
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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Specificații

ISBN-13: 9783658290160
ISBN-10: 3658290161
Pagini: 161
Ilustrații: XV, 161 p. 56 illus.
Dimensiuni: 148 x 210 mm
Greutate: 0.22 kg
Ediția:1st ed. 2020
Editura: Springer Fachmedien Wiesbaden
Colecția Springer Vieweg
Seria Schriftenreihe der Institute für Systemdynamik (ISD) und optische Systeme (IOS)

Locul publicării:Wiesbaden, Germany

Cuprins

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.

Notă biografică

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.

Textul de pe ultima copertă

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.

Caracteristici

Demonstrates that machine learning can be a viable part of the CAD reverse engineering pipeline Scientific-technical study