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2013 | Buch

Introduction Kapitel lesen Erstes Kapitel lesen

3D Surface Reconstruction

Multi-Scale Hierarchical Approaches

verfasst von: Francesco Bellocchio, N. Alberto Borghese, Stefano Ferrari, Vincenzo Piuri

Verlag: Springer New York

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

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.

Inhaltsverzeichnis

Frontmatter
Chapter 1. Introduction
Abstract
What is a 3D model? When and why are they used for? How are they created? This chapter presents a brief overview to understand the usefulness of 3D models and how they can be computed from a physical object. The pipeline that creates a 3D model is presented and each step is concisely described. In the rest of the book the main steps of this pipeline are analyzed and described in depth.
Francesco Bellocchio, N. Alberto Borghese, Stefano Ferrari, Vincenzo Piuri
Chapter 2. Scanner systems
Abstract
Generally, the first step in the creation of a 3D model consists in capturing the geometric and color information of the physical object. Objects can be as small as coins or as large as buildings, they can be still or move while scanning, and this has prompted the development of very different technologies and instruments. The aim of this chapter is to present such technologies to explain the techniques on which 3D scanners are based. Comparison in terms of accuracy, speed and applicability is reported, in order to understand advantages and disadvantages of the different approaches. How to use the information captured to compute the 3D model will be discussed in the next chapters.
Francesco Bellocchio, N. Alberto Borghese, Stefano Ferrari, Vincenzo Piuri
Chapter 3. Reconstruction
Abstract
Once the geometrical data of a physical object have been sampled on its surface, the next step is the generalization of the sampled data to obtain a continuous description of the object surface, to which visual attributes (like color, textures, and reflectance) can be associated. In this chapter an overview of the techniques used for generalization is presented. These can be subdivided into two broad families: volumetric and surface fitting methods.
Francesco Bellocchio, N. Alberto Borghese, Stefano Ferrari, Vincenzo Piuri
Chapter 4. Surface fitting as a regression problem
Abstract
A brief overview of the methods for surface reconstruction has been presented in the previous chapters. In this chapter the attention will be focused on a particular class of methods that see surface reconstruction as a multivariate approximation problem. Some of the most popular techniques of this kind will be presented and pros and cons will be discussed. An evolution of two of these techniques, namely Radial Basis Function Neural Networks and Support Vector Machines, will be the topic of the next two chapters.
Francesco Bellocchio, N. Alberto Borghese, Stefano Ferrari, Vincenzo Piuri
Chapter 5. Hierarchical Radial Basis Functions Networks
Abstract
In this chapter a particular kind of neural model, namely the Hierarchical Radial Basis Function Network, is presented as an effective hierarchical network organization. In a similar way, in the next chapter another kind of multi-scale model, namely Support Vector Machines, will be presented.
Francesco Bellocchio, N. Alberto Borghese, Stefano Ferrari, Vincenzo Piuri
Chapter 6. Hierarchical Support Vector Regression
Abstract
In the previous chapter the RBFN model and the advantages of a hierarchical version for surface reconstruction has been presented. In a similar way in this chapter another paradigm, Support Vector Regression (SVR), and its hierarchical version, Hierarchical Support Vector Regression (HSVR) that allows an efficient construction of the approximating surface, are introduced. Thanks to the hierarchical structure, the model can be better applied to 3D surface reconstruction giving a new, more robust and faster configuration procedure.
Francesco Bellocchio, N. Alberto Borghese, Stefano Ferrari, Vincenzo Piuri
Chapter 7. Conclusion
Abstract
We review here the main characteristics of the HRBF and HSVR models. Possible future developments based on parallelization and GPU implementation are described.
Francesco Bellocchio, N. Alberto Borghese, Stefano Ferrari, Vincenzo Piuri
Backmatter
Metadaten
Titel
3D Surface Reconstruction
verfasst von
Francesco Bellocchio
N. Alberto Borghese
Stefano Ferrari
Vincenzo Piuri
Copyright-Jahr
2013
Verlag
Springer New York
Electronic ISBN
978-1-4614-5632-2
Print ISBN
978-1-4614-5631-5
DOI
https://doi.org/10.1007/978-1-4614-5632-2