Guide to Computational Geometry Processing : Foundations, Algorithms, and Methods

J. Andreas Bærentzen

ISBN 10: 1447160312 / ISBN 13: 9781447160311

Editore: Springer London, Springer London, 2014

Riguardo questo articolo

Druck auf Anfrage Neuware - Printed after ordering - This book reviews the algorithms for processing geometric data, with a practical focus on important techniques not covered by traditional courses on computer vision and computer graphics. Features: presents an overview of the underlying mathematical theory, covering vector spaces, metric space, affine spaces, differential geometry, and finite difference methods for derivatives and differential equations; reviews geometry representations, including polygonal meshes, splines, and subdivision surfaces; examines techniques for computing curvature from polygonal meshes; describes algorithms for mesh smoothing, mesh parametrization, and mesh optimization and simplification; discusses point location databases and convex hulls of point sets; investigates the reconstruction of triangle meshes from point clouds, including methods for registration of point clouds and surface reconstruction; provides additional material at a supplementary website; includes self-study exercises throughout the text.

Codice articolo 9781447160311

Dati bibliografici

• Titolo: Guide to Computational Geometry Processing : Foundations, Algorithms, and Methods
• Editore: Springer London, Springer London
• Data di pubblicazione: 2014
• Lingua: Inglese
• ISBN 10: 1447160312
• ISBN 13: 9781447160311
• Rilegatura: Taschenbuch
• Condizione: Neu
• Dimensioni: 235 L x 155 A x 19 P (millimetri) 235 millimetri di larghezza per 155 millimetri di altezza per 19 millimetri di profondità
• Peso: 522 grammi

Descrizione articolo

Riassunto

This book reviews the algorithms for processing geometric data, with a practical focus on important techniques not covered by traditional courses on computer vision and computer graphics. Features: presents an overview of the underlying mathematical theory, covering vector spaces, metric space, affine spaces, differential geometry, and finite difference methods for derivatives and differential equations; reviews geometry representations, including polygonal meshes, splines, and subdivision surfaces; examines techniques for computing curvature from polygonal meshes; describes algorithms for mesh smoothing, mesh parametrization, and mesh optimization and simplification; discusses point location databases and convex hulls of point sets; investigates the reconstruction of triangle meshes from point clouds, including methods for registration of point clouds and surface reconstruction; provides additional material at a supplementary website; includes self-study exercises throughout the text.

Dalla quarta di copertina

Optical scanning is rapidly becoming ubiquitous. From industrial laser scanners to medical CT, MR and 3D ultrasound scanners, numerous organizations now have easy access to optical acquisition devices that provide huge volumes of image data. However, the raw geometry data acquired must first be processed before it is useful.

This Guide to Computational Geometry Processing reviews the algorithms for processing geometric data, with a practical focus on important techniques not covered by traditional courses on computer vision and computer graphics. This is balanced with an introduction to the theoretical and mathematical underpinnings of each technique, enabling the reader to not only implement a given method, but also to understand the ideas behind it, its limitations and its advantages.

Topics and features:

• Presents an overview of the underlying mathematical theory, covering vector spaces, metric space, affine spaces, differential geometry, and finite difference methods for derivatives and differential equations
• Reviews geometry representations, including polygonal meshes, splines, and subdivision surfaces
• Examines techniques for computing curvature from polygonal meshes
• Describes algorithms for mesh smoothing, mesh parametrization, and mesh optimization and simplification
• Discusses point location databases and convex hulls of point sets
• Investigates the reconstruction of triangle meshes from point clouds, including methods for registration of point clouds and surface reconstruction
• Provides additional material at a supplementary website
• Includes self-study exercises throughout the text

Graduate students will find this text a valuable, hands-on guide to developing key skills in geometry processing. The book will also serve as a useful reference for professionals wishing to improve their competency in this area.