Description

Book Synopsis
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.

Table of 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.

Machine Learning Methods for Reverse Engineering of Defective Structured Surfaces

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    A Paperback by Pascal Laube

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      View other formats and editions of Machine Learning Methods for Reverse Engineering of Defective Structured Surfaces by Pascal Laube

      Publisher: Springer Fachmedien Wiesbaden
      Publication Date: Publication Date: 03/01/2020
      ISBN13: 9783658290160, 978-3658290160
      ISBN10: 3658290161

      Description

      Book Synopsis
      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.

      Table of 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.

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