Laube Machine Learning Methods for Reverse Engineering of Defective Structured Surfaces

1. Auflage 2020
ISBN: 978-3-658-29017-7
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 161 Seiten, Web PDF

Reihe: Schriftenreihe der Institute für Systemdynamik (ISD) und optische Systeme (IOS)

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.

Zielgruppe

Research

Autoren/Hrsg.

Laube, Pascal

Inhaltsverzeichnis

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.

Über Autor(innen)

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.