Machine Learning Methods for Reverse Engineering of Defective Structured Surfaces - Brossura

Laube, Pascal

Brossura

ISBN 10: 3658290161 ISBN 13: 9783658290160

Casa editrice: Springer Vieweg, 2020

Riassunto

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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Informazioni sull?autore

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.

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.

Dettagli dell'edizione

• Editore: Springer Vieweg
• Data di pubblicazione: 2020
• Lingua: Inglese
• ISBN 10: 3658290161
• ISBN 13: 9783658290160
• Rilegatura: Copertina flessibile
• Numero edizione: 1
• Numero di pagine: 180
• Contatto del produttore: non disponibile
• Persona responsabile: CMN Printpool Inh. Mathis Neumann; https://www.also.com/ec/cms5/en_6000/6000/index.jsp; ; Friedrich-Karl-Str. 9; Hamburg; Germania