Chapter 1. Fundamentals
1.1. Introduction
1.2. A short history
1.2.1. Pinhole model
1.2.2. 3D and binocular vision
1.2.3. Reconstruction
1.3. Stereopsis and 3D physiological aspects
1.4. 3D computer vision
1.5. Conclusion
1.6. Bibliography
Chapter 2. Digital cameras: definitions and principles
2.1. Introduction
2.2. Acquiring light: physics fundamentals
2.2.1. Radiometry and photometry
2.2.1.1. Scene illumination
2.2.2. Wavelengths and color spaces
2.3. Digital cameras
2.3.1. Optical components
2.3.1.1. Camera optics
2.3.1.2. Errors and corrections
2.3.2. Electronic components
2.3.2.1. Camera sensors
2.3.2.2. Digital noise and noise removal algorithms
2.3.3. Main camera functions and control
2.3.3.1. Autobracketing
2.3.4. Image storage formats
2.4. Camera, human vision and color
2.4.1. Adapting optics and electronic to human perception
2.4.2. Color control
2.4.2.1. Camera response
2.4.2.2. Color characterization
2.5. Outperforming
2.5.1. HDR imaging
2.5.2. Hyperspectral acquisition
2.6. Conclusion
2.7. Bibliography
Chapter 3: Multiview acquisition systems
3.1. Introduction: what is a multiview acquisition system?
3.2. Binocular systems
3.3. Lateral or directional multiview systems
3.4. Surrounding or omnidirectional multiview systems
3.5. Hybrid systems: RGBZ and TOF
3.6. Conclusion
3.7. Bibliography
Chapter 4: Shooting and viewing geometry for 3D TV
4.1. Introduction
4.2. Output geomerty of imaginary relief
4.2.1. Description
4.2.2. Possible modelling
4.3. Capture geometry of imaginary relief
4.3.1. Type of geometry to be used
4.3.2. Possible modelling
4.4. Link between output and capture geometry
4.4.1. Geometric characterization of imaginary relief experience
4.4.2. Distortion models
4.5. Methodology for specifying multiscopic acquisition
4.5.1. Controlling relief distortion
4.5.2. Perfect relief effect
4.6. Implementation in OpenGL
4.7. Conclusion
4.8. Bibliography
Chapter 5: Geometric and colorimetric calibration and rectification
5.1. Introduction
5.2. Camera calibration
5.2.1. Introduction
5.2.2.Camera model
5.2.3. Calibration with a target
5.2.4. Automatic methods
5.3. Radial distortion
5.3.1. Introduction
5.3.2. When should distortion be corrected?
5.3.3. Radiale distortion correction models
5.4. Image rectification
5.4.1. Introduction
5.4.1.1. Problematics
5.4.2. Image-based methods
5.4.3. Camera-based methods
5.4.4. Rectification of more than 2 images simultaneously
5.5. Camera colorimetric aspects
5.5.1. Applyed colorimetry
5.5.2. Camera colorimetric calibration
5.5.2.1. Estimation of F(k) and S(k)
5.5.2.2. In practice
5.6. Conclusion
5.7. Bibliography
Chapter 6: Feature points detection and image matching
6.1. Introduction
6.2. Feature points
6.2.1. Points detectors
6.2.1.1. Differential operators: Autocorrelation, Harris and Hessian
6.2.1.2. Scale invariance using multi-scale analysis
6.2.1.3. Corner intensity model
6.2.2. Contours and feature points detection
6.2.2.1. Shapes detectors
6.2.2.2. Curvature and scale space
6.2.3. Stable regions: IBR, MSER
6.3. Feature point descriptors
6.3.1. Scale-invariant feature transform: SIFT
6.3.2. Gradient Local Orientation Histogram: GLOH
6.3.3. DAISY descriptor
6.3.4. Speeded Up Robust Features: SURF
6.3.5. Multi-scale Oriented PatcheS: MOPS
6.3.6. Shape context
6.4. Image matching
6.4.1. Descriptors matching
6.4.2. Estimation of the geometric transform: matches grouping
6.4.2.1. Generalized Hough Transform
6.4.2.2. Graph matching
6.4.2.3. RANSAC and variants
6.5. Conclusion
Chapter 7: Multi and Stereoscopic matching, depth and disparity
7.1. Introduction
7.2. Difficulties, primitives and density of stereoscopy matching
7.2.1. Difficulties
7.2.2. Primitives and density
7.3. Simplified geometry and disparity
7.4. Description of stereoscopic and multiscopic methods
7.4.1. Algorithms of local and global matching
7.4.2. Principal constraints
7.4.3. Energy costs
7.5. Methods with explicit consideration of occultations
7.5.1. Stereoscopic local method – propagation of seeds
7.5.1.1 Initialization of germs
7.5.1.2 Approach by propagation
7.5.1.3 Regulation by region sounding
7.5.2 Multiscopic global method
7.5.2.1 Formulation of multiscopic matching
7.5.2.2. Energy function and constraint of geometric consistency
7.5.2.3. Global selection and partition construction
7.5.2.4. Results
7.6. Conclusion
7.7. Bibliography
Chapter 8: Multiview reconstruction
8.1. Problematics
8.2. Visual hull-based reconstruction
8.2.1. Methods to extract visual hulls
8.2.2. Reconstruction methods
8.2.3. Improving volume reconstruction
8.2.3.1. Voxel Coloring
8.2.3.2. Space Carving
8.3. Industrial implementation
8.3.1. Hardware acceleration
8.3.2. Results
8.4. Temporal structuration of reconstructions
8.4.1. Extraction of a generic skeleton
8.4.2. Computation of motion fields
8.5. Conclusion
8.6. Bibliography
Chapter 9: Synthesis of intermediate views
9.1. Introduction
9.2. Interpolation/extrapolation view synthesis
9.2.1. Direct and inverse projections
9.2.1.1. Equations of direct projection
9.2.1.2. Direct projection artefacts
9.2.1.3. Inverse projection inverse
9.2.2. Limiting view synthesis artefacts
9.2.2.1. Cracks
9.2.2.2. Ghost outlines
9.2.2.3. Open zones
9.2.3. View interpolation
9.2.3.1. Fusion of virtual views
9.2.3.2. Detection and smoothing of interpolation artefacts
9.2.3.3. Float textures
9.2.3.4. View extrapolation
9.3. Open zone filling
9.3.1. State of the art on 2D inpainting techniques
9.3.1.1. Diffusion-based methods
9.3.1.2. Similarity-based methods
9.3.2. 3D inpainting
9.3.2.1. Crimini et al. [CRI 04] extension to 3D context
9.3.2.2. Global optimisation-based inpainting
9.4. Conclusion
9.5. Bibliography
Chapter 10: Encoding multiview videos
10.1 Introduction
10.2 Compression of stereoscopic videos
10.2.1 3D formats
10.2.1.1 Frame compatible
10.2.1.2 Mixed Resolution Stereo
10.2.1.3 2D-plus-depth
10.2.2 Associated coding techniques
10.2.2.1 Simulcast
10.2.2.2 MPEG-C and H.264/AVC APS
10.2.2.3 H.264/MVC Stereo Profile
10.3 Compression of multiview videos
10.3.1 3D formats
10.3.1.1 MVV and MVD
10.3.1.2 LDI and LDV
10.3.1.3 DES
10.3.2 Associated coding techniques
10.3.2.1 H.264/MVC Multiview Profile
10.3.2.2 LDI-dedicated methods
10.4 Conclusion
10.5 Bibliography
Chapter 11: 3D mesh compression
11.1. Introduction
11.2. Background on coding: The rate-distortion theory
11.3. Multi-resolution coding of surface meshes
11.4. Topological and progressive coding
11.4.1. Mono-resolution compression
11.4.2. Multi-resolution compression
11.4.2.1. Connectivity-driven approaches
11.4.2.2. Geometry-driven approaches
11.5. Mesh Sequences Compression
11.5.1. Definitions
11.5.2. Spatio-temporal prediction methods
11.5.3. Segmentation based methods
11.5.4. Transformation based methods
11.6. Quality assessment: classical and perceptual metrics
11.6.1. Classical metrics
11.6.2. Perceptual metrics
11.7. Conclusion
11.8. Bibliography
Chapter 12: Depth Video Coding Technologies
12.1 Introduction
12.2 Analysis of a depth map characteristics
12.3 Depth video coding tools
12.3.1 Tools that exploit the inherent characteristics of depth maps
12.3.1.1 Above block-level coding tools
12.3.1.2 Block-level coding tools
12.3.2 Tools that exploit the correlation with the associated texture
12.3.2.1 Prediction mode inheritance / selection
12.3.2.2 Prediction information inheritance
12.3.2.3 Spatial transforms
12.3.3 Tools that optimize depth video coding for the virtual views quality
12.3.3.1 View synthesis optimization
12.3.3.2 Distortion models
12.4 Conclusion
12.5 Bibliography
Chapter 13. Stereoscopic watermarking
13.1. Introduction
13.2. Stereoscopic watermarking constraints
13.2.1. Theoretical framework
13.2.2. Properties
13.2.2.1. Transparency
13.2.2.2. Robustness
13.2.2.3. Data payload
13.2.2.4. Computational cost
13.2.3. Corpus
13.2.3.1. Design criteria
13.2.3.2. Processed corpora
13.2.4. Conclusion
13.3. State-of-the-art on stereoscopic watermarking
13.4. Comparative study
13.4.1. Transparency
13.4.1.1. Subjective evaluation
13.4.1.2. Objective evaluation
13.4.2. Robustness
13.4.3. Computational cost
13.4.4. Conclusion
13.5. Conclusion and perspectives
13.6. References
Chapter 14: 3D HD TV and autostereoscopy
14.1.Introduction
14.2.Technological principles
14.2.1.Stereoscopic devices with glasses
14.2.2.Autostereoscopic devices
14.2.3.Optics
14.2.4.Mesurements of autostereoscopic display
14.3. Mixing filters
14.4.Generating and enterlacing views
14.4.1.Virtual view generation
14.4.2.Enterlacing views
14.5. Futur developments
14.6.Conclusion
14.7.Bibliography
Chapter 15: Augmented and/or mixed reality
15.1. Introduction
15.2. Real-time pose computation
15.2.1. Requirements for pose computation
15.2.2. Model/image feature matching
15.2.2.1. Iterative tracking methods
15.2.2.2. Recognition methods
15.2.2.3. Real-time constraint
15.2.3. Pose computation: the main PnP algorithms
15.2.3.1. Reprojection error minimization
15.2.3.2. Direct methods
15.2.4. Pose computation and planar surfaces
15.3. Model acquisition
15.3.1. Offline modelization
15.3.2. Online modelization
15.4. Conclusion
15.5. Bibliography
Chapter 16. Visual comfort and visual fatigue for stereoscopic restitution
16.1. Introduction
16.2. Visual comfort and fatigue: definition and evaluation
16.2.1. Visual fatigue
16.2.2. Visual comfort and discomfort
16.2.3. Assessment and evaluation of fatigue and discomfort
16.3. Symptoms and signs of fatigue and discomfort
16.3.1. Ocular and oculomotor fatigue
16.3.2. Cognitive fatigue
16.3.3. Symptoms and signs of discomfort
16.4. Sources of fatigue and discomfort
16.4.1. Ocular constraints
16.4.2. Cognitive constraints
16.5. Application to 3D displays and contents
16.5.1. Comfort zone
16.5.2. Restitution defects
16.5.3. Accommodation and blur
16.5.4. Visual attention
16.5.5. Null or erroneous motion parallax
16.5.6. Exposure duration and training effects
16.6. Predicting visual fatigue and discomfort: emerging models
16.7. Conclusion
16.8. Bibliography/references
Chapter 17: 2D to 3D conversion
17.1.Introduction
17.2. 2D-3D conversion workflow
17.3.Content preparation for conversion
17.3.1.Depth script
17.3.2. Video advantage on fix images
17.3.3. Automatic conversion decoy
17.3.4.Special cases of automatic conversion
17.3.5.Optimal content for 2D-3D conversion
17.4. Conversion steps
17.4.1. Segmentation step
17.4.2.Depth map, computation and propagation
17.4.3.Missing image generation
17.5.3D-3D conversion
17.6.Conclusion
17.7.Bibliography
Chapter 18. 3D-model retrieval
18.1. Introduction
18.2. General principles of shape retrieval
18.3. Global 3D-shape descriptors
18.3.1. Shape descriptor histogram
18.3.2. Spherical harmonics
18.4. 2D-view based methods
18.5. Local 3D-shape descriptors
18.5.1. 3D-shape spectrum descriptor
18.5.2. 3D-shape context
18.5.3. Spin-images
18.5.4. Heat cernel Signature
18.6. 3D-shape similarities
18.6.1. Reeb graphs
18.6.2. Bof-of-Words
18.7. 3D-shape retrieval in 3D-videos
18.7.1. Action recognition in 3D-videos
18.7.2. Facial expression recognition in 3D-videos
18.8. Performance evaluation of shape retrieval methods
18.8.1. Statistic tools for evaluation
18.8.2. Benchmarks
18.9. Applications
18.9.1. Browsing in a collection of 3D-models
18.9.2. Modeling by example
18.9.3. Decision aid
18.9.4. 3D-face recognition
18.10. Conclusion
18.11. References
Chapter 19: 3D HDR images and videos: acquisition and restitution
19.1. Introduction
19.2. HDR and 3D acquisition
19.2.1. Subspace 1D: HDR images
19.2.2. Subspace 2D: HDR videos
19.2.3. Subspace 2D: 3DHDR images
19.2.3.1. Stereo matching for HDR reconstruction
19.2.3.2. Discussion on color data consistency
19.2.4. Extension to the whole space: 3DHDR videos
19.3. 3D HDR rendering
19.3.1. Rendering on a 3D-dedicated display
19.3.2. Rendering on an HDR-dedicated display
19.4. Conclusion
19.5. Bibliography
Chapter 20: 3D TV visualization for life sciences
20.1.Introduction
20.2.Scientific visualization
20.2.1. 3D construction
20.2.2.Interactivity
20.2.3.3D visualization
20.3.Medical imaging
20.3.1.Volumic visualization
20.3.2.3D medical imaging
20.3.2.1.Teaching
20.3.2.2.Diagnostic
20.3.2.3.Therapy
20.4. Molecular modeling
20.4.1.Classical modes of visualization
20.4.2.Molecular modeling in relief
20.5.Conclusion
20.6.Bibliography
Chapter 21: 3D reconstruction of sport scenes
21.1.Introduction
21.2.Automatic selection of region of interest
21.2.1.Region of interest role and caracteristics
21.2.2.Color space segmentation
21.2.3.Spacial consistency
21.3.Primitive extraction by HOUGH transform
21.3.1.Ellipsoid segment detection
21.4.Primitive/model matching
21.4.1.Line beams
21.5.Conclusion
21.6.Bibliography
Chapter 22: Experimental, live retransmissions in stereoscopic 3D (S-3D)
22.1.Introduction
22.2.Show retransmissions
22.3.Surgery retransmissions
22.4. Steadicam magazine retransmissions
22.5.Transatlantic video-presentation retransmission
22.6.Bicycle competition retransmissions
22.7.Conclusion
22.8.Bibliography
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