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.
الكلمات المفتاحية: Computer science / Machine Learning Methods / CaD Reverse Engineering / Defective Structured Surfaces / Surface Reconstruction / Geometric Modeling / Surface Approximation / Surface Parametrization
