3D Surface Reconstruction: Multi-Scale Hierarchical Approaches presents methods to model 3D objects in an incremental way so as to capture more finer details at each step. The configuration of the model parameters, the rationale and solutions are described and discussed in detail so the reader has a strong understanding of the methodology. Modeling starts from data captured by 3D digitizers and makes the process even more clear and engaging. Innovative approaches, based on two popular machine learning paradigms, namely Radial Basis Functions and the Support Vector Machines, are also introduced. These paradigms are innovatively extended to a multi-scale incremental structure, based on a hierarchical scheme. The resulting approaches allow readers to achieve high accuracy with limited computational complexity, and makes the approaches appropriate for online, real-time operation. Applications can be found in any domain in which regression is required. 3D Surface Reconstruction: Multi-Scale Hierarchical Approaches is designed as a secondary text book or reference for advanced-level students and researchers in computer science. This book also targets practitioners working in computer vision or machine learning related fields.

3D Surface Reconstruction: Multi-Scale Hierarchical Approaches presents methods to model 3D objects in an incremental way so as to capture more finer details at each step. The configuration of the model parameters, the rationale and solutions are described and discussed in detail so the reader has a strong understanding of the methodology. Modeling starts from data captured by 3D digitizers and makes the process even more clear and engaging. Innovative approaches, based on two popular machine learning paradigms, namely Radial Basis Functions and the Support Vector Machines, are also introduced. These paradigms are innovatively extended to a multi-scale incremental structure, based on a hierarchical scheme. The resulting approaches allow readers to achieve high accuracy with limited computational complexity, and makes the approaches appropriate for online, real-time operation. Applications can be found in any domain in which regression is required. 3D Surface Reconstruction: Multi-Scale Hierarchical Approaches is designed as a secondary text book or reference for advanced-level students and researchers in computer science. This book also targets practitioners working in computer vision or machine learning related fields.

7. Future directions -- Bibliography -- Authors' biographies.

"This book provides developers and scholars with an extensive collection of research articles in the expanding field of 3D reconstruction, investigating the concepts, methodologies, applications and recent developments in the field of 3D reconstruction"--

This book constitutes the strictly refereed post-workshop proceedings of the European Workshop on 3D Structure from Multiple Images of Large-Scale Environments, SMILE'98, held in conjunction with ECCV'98 in Freiburg, Germany, in June 1998. The 21 revised full papers presented went through two cycles of reviewing and were carefully selected for inclusion in the book. The papers are organized in sections on multiview relations and correspondence search, 3D structure from multiple images, callibration and reconstruction using scene constraints, range integration and augmented reality application.

The six-volume set comprising the LNCS volumes 11129-11134 constitutes the refereed proceedings of the workshops that took place in conjunction with the 15th European Conference on Computer Vision, ECCV 2018, held in Munich, Germany, in September 2018.43 workshops from 74 workshops proposals were selected for inclusion in the proceedings. The workshop topics present a good orchestration of new trends and traditional issues, built bridges into neighboring fields, and discuss fundamental technologies and novel applications.

Many applications in science and engineering require a digital model of a real physical object. Advanced scanning technology has made it possible to scan such objects and generate point samples on their boundaries. This book, first published in 2007, shows how to compute a digital model from this point sample. After developing the basics of sampling theory and its connections to various geometric and topological properties, the author describes a suite of algorithms that have been designed for the reconstruction problem, including algorithms for surface reconstruction from dense samples, from samples that are not adequately dense and from noisy samples. Voronoi- and Delaunay-based techniques, implicit surface-based methods and Morse theory-based methods are covered. Scientists and engineers working in drug design, medical imaging, CAD, GIS, and many other areas will benefit from this first book on the subject.