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Machine Learning Methods for Reverse Engineering of Defective Structured Surfaces

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Machine Learning Methods for Reverse Engineering of Defective Structured Surfaces/ by Pascal Laube.
Author:	Laube, Pascal.
Description:	XV, 161 p. 56 illus.online resource. :
Contained By:	Springer Nature eBook
Subject:	Machine learning. -
Online resource:	https://doi.org/10.1007/978-3-658-29017-7
ISBN:	9783658290177

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

Machine Learning Methods for Reverse Engineering of Defective Structured Surfaces[electronic resource] /by Pascal Laube. - 1st ed. 2020. - XV, 161 p. 56 illus.online resource. - Schriftenreihe der Institute für Systemdynamik (ISD) und optische Systeme (IOS),2661-8087. - Schriftenreihe der Institute für Systemdynamik (ISD) und optische Systeme (IOS),.

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.

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.

ISBN: 9783658290177

Standard No.: 10.1007/978-3-658-29017-7doiSubjects--Topical Terms:

561253
Machine learning.

LC Class. No.: Q325.5-.7

Dewey Class. No.: 006.31

Machine Learning Methods for Reverse Engineering of Defective Structured Surfaces
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