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Machine Learning Methods for Reverse Engineering of Defective Structured Surfaces

  • Book
  • © 2020

Overview

Authors:
  1. Pascal Laube

    1. Konstanz, Germany

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  • Demonstrates that machine learning can be a viable part of the CAD reverse engineering pipeline
  • Scientific-technical study

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Table of contents (6 chapters)

  1. Front Matter

    Pages I-XV
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  2. Introduction

    • Pascal Laube
    Pages 1-11

  3. Fundamentals

    • Pascal Laube
    Pages 13-49

  4. Parametrization in Curve and Surface Approximation

    • Pascal Laube
    Pages 51-95

  5. Classification of Geometric Primitives in Point Clouds

    • Pascal Laube
    Pages 97-120

  6. CNN Texture Synthesis for High-Resolution Image Inpainting

    • Pascal Laube
    Pages 121-141

  7. Concluding Remarks & Outlook

    • Pascal Laube
    Pages 143-146

  8. Back Matter

    Pages 147-161
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Keywords

About this book

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.

Authors and Affiliations

  • Konstanz, Germany

    Pascal Laube

About 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.

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