Machine Learning Methods for Reverse Engineering of Defective Structured Surfaces
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Machine Learning Methods for Reverse Engineering of Defective Structured Surfaces, Springer Vieweg
Von Pascal Laube, im heise Shop in digitaler Fassung erhältlich
Produktinformationen "Machine Learning Methods for Reverse Engineering of Defective Structured Surfaces"
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.
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.
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.
Artikel-Details
- Anbieter:
- Springer Vieweg
- Autor:
- Pascal Laube
- Artikelnummer:
- 9783658290177
- Veröffentlicht:
- 02.01.20