“This book guides you in the journey of 3D modeling from the theory with elegant mathematics to applications with beautiful 3D model pictures. Written in a simple, straightforward, and concise manner, readers will learn the state of the art of 3D reconstruction and modeling.” ―Professor Takeo Kanade, Carnegie Mellon University The computer vision and graphics communities use different terminologies for the same ideas. This book provides a translation, enabling graphics researchers to apply vision concepts, and vice-versa, independence of chapters allows readers to directly jump into a specific chapter of interest, compared to other texts, gives more succinct treatment overall, and focuses primarily on vision geometry. Image-Based Modeling is for graduate students, researchers, and engineers working in the areas of computer vision, computer graphics, image processing, robotics, virtual reality, and photogrammetry.
Author(s): Long Quan
Edition: 1
Publisher: Springer
Year: 2010
Language: English
Pages: 269
City: New York
Tags: 3D Modeling; GIS; Google Earth; Image-Based Rendering; Algorithms; Image Processing; Image-based Modeling; Photogrammetry; Segmentation; Single-view Modeling; Surface Modeling; Surface Reconstruction; Tree Modeling; Vision Geometry
Foreword
Preface
Acknowledgements
Notation
Contents
Introduction
Part I
Geometry prerequisite
2.1 Introduction
2.2 Projective geometry
2.2.1 The basic concepts
2.2.2 Projective spaces and transformations
2.2.3 Affine and Euclidean specialization
2.3 Algebraic geometry
2.3.1 The simple methods
2.3.2 Ideals, varieties, and Gr¨obner bases
2.3.3 Solving polynomial equations with Gr¨obner bases
Multi-view geometry
3.1 Introduction
3.2 The single-view geometry
3.2.1 What is a camera?
3.2.2 Where is the camera?
3.2.3 The DLT calibration
3.2.4 The three-point pose algorithm
3.3 The uncalibrated two-view geometry
3.3.1 The fundamental matrix
3.3.2 The seven-point algorithm
3.3.3 The eight-point linear algorithm
3.4 The calibrated two-view geometry
3.4.1 The essential matrix
3.4.2 The five-point algorithm
3.5 The three-view geometry
3.5.1 The trifocal tensor
3.5.2 The six-point algorithm
3.5.3 The calibrated three views
3.6 The N-view geometry
3.6.1 The multi-linearities
3.6.2 Auto-calibration
3.7 Discussions
3.8 Bibliographic notes
Part II
Feature point
4.1 Introduction
4.2 Points of interest
4.2.1 Tracking features
4.2.2 Matching corners
4.2.3 Discussions
4.3 Scale invariance
4.3.1 Invariance and stability
4.3.2 Scale, blob and Laplacian
4.3.3 Recognizing SIFT
4.4 Bibliographic notes
Structure from Motion
5.1 Introduction
5.1.1 Least squares and bundle adjustment
5.1.2 Robust statistics and RANSAC
5.2 The standard sparse approach
5.2.1 A sequence of images
5.2.2 A collection of images
5.3 The match propagation
5.3.1 The best-first match propagation
5.3.2 The properties of match propagation
5.3.3 Discussions
5.4 The quasi-dense approach
5.4.1 The quasi-dense resampling
5.4.2 The quasi-dense SFM
5.4.3 Results and discussions
5.5 Bibliographic notes
Part III
Surface modeling
6.1 Introduction
6.2 Minimal surface functionals
6.3 A unified functional
6.4 Level-set method
6.5 A bounded regularization method
6.6 Implementation
6.7 Results and discussions
6.8 Bibliographic notes
Hair modeling
7.1 Introduction
7.2 Hair volume determination
7.3 Hair fiber recovery
7.3.1 Visibility determination
7.3.2 Orientation consistency
7.3.3 Orientation triangulation
7.4 Implementation
7.5 Results and discussions
7.6 Bibliographic notes
Tree modeling
8.1 Introduction
8.2 Branche recovery
8.2.1 Reconstruction of visible branches
8.2.2 Synthesis of occluded branches
8.2.3 Interactive editing
8.3 Leaf extraction and reconstruction
8.3.1 Leaf texture segmentation
8.3.2 Graph-based leaf extraction
8.3.3 Model-based leaf reconstruction
8.4 Results and discussions
8.5 Bibliographic notes
Fac¸ade modeling
9.1 Introduction
9.2 Fac¸ade initialization
9.2.1 Initial flat rectangle
9.2.2 Texture composition
9.2.3 Interactive refinement
9.3 Fac¸ade decomposition
9.3.1 Hidden structure discovery
9.3.2 Recursive subdivision
9.3.3 Repetitive pattern representation
9.3.4 Interactive subdivision refinement
9.4 Fac¸ade augmentation
9.4.1 Depth optimization
9.4.2 Cost definition
9.4.3 Interactive depth assignment
9.5 Fac¸ade completion
9.6 Results and discussions
9.7 Bibliographic notes
Building modeling
10.1 Introduction
10.2 Pre-processing
10.3 Building segmentation
10.3.1 Supervised class recognition
10.3.2 Multi-view semantic segmentation
10.4 Building partition
10.4.1 Global vertical alignment
10.4.2 Block separator
10.4.3 Local horizontal alignment
10.5 Fac¸ade modeling
10.5.1 Inverse orthographic composition
10.5.2 Structure analysis and regularization
10.5.3 Repetitive pattern rediscovery
10.5.4 Boundary regularization
10.6 Post-processing
10.7 Results and discussions
10.8 Bibliographic notes
List of Algorithms
List of Figures
References
Index
