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.
Reihe
Auflage
Sprache
Verlagsort
Verlagsgruppe
Springer Fachmedien Wiesbaden GmbH
Zielgruppe
Illustrationen
56
56 s/w Abbildungen
XV, 161 p. 56 illus.
Maße
Höhe: 210 mm
Breite: 148 mm
Dicke: 11 mm
Gewicht
ISBN-13
978-3-658-29016-0 (9783658290160)
DOI
10.1007/978-3-658-29017-7
Schweitzer Klassifikation
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.
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.
