110,39 €
Machine Learning Methods for Reverse Engineering of Defective Structured Surfaces
Machine Learning Methods for Reverse Engineering of Defective Structured Surfaces
  • Išparduota
Machine Learning Methods for Reverse Engineering of Defective Structured Surfaces
Machine Learning Methods for Reverse Engineering of Defective Structured Surfaces
El. knyga:
110,39 €
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.

Machine Learning Methods for Reverse Engineering of Defective Structured Surfaces (el. knyga) (skaityta knyga) | knygos.lt

Atsiliepimai

(4.00 Goodreads įvertinimas)
Įvertink ir tu! Visi atsiliepimai

Formatai:

110,39 € El. knyga

Aprašymas

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.

110,39 €
Prisijunkite ir už šią prekę
gausite 1,10 Knygų Eurų! ?

Elektroninė knyga:
Atsiuntimas po užsakymo akimirksniu! Skirta skaitymui tik kompiuteryje, planšetėje ar kitame elektroniniame įrenginyje.

Mažiausia kaina per 30 dienų: 110,39 €

Mažiausia kaina užfiksuota: Kaina nesikeitė


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.

Atsiliepimai

  • Atsiliepimų nėra
0 pirkėjai įvertino šią prekę.
5
0%
4
0%
3
0%
2
0%
1
0%
(rodomas nebus)
Knygos.lt komentarų taisyklės