Customer Reviews

Principles of Digital Image Synthesis (The Morgan Kaufmann Series in Computer Graphics) 2 Volume Set

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Back in the old days, Computer Graphics was a big bag of tricks for making cool images. To make the pictures look better and better, the research community stumbled into areas we didn't originally know were important. We now see that clipping, viewports, line-drawing, and specular lights are not so fundamental. Instead, we're understanding that items in the bag of graphics tricks were often shortcuts to solving an integral equation for heat transfer. Who would have thought it?

This textbook is the first comprehensive treatment of Computer Graphics to convey the deeper understanding that researchers have finally begun to make peace with. It's not always easy. That marginal lecture on de-aliasing in your graphics class? It turns out to be hugely significant. Sampling and reconstruction pervade graphics algorithms, and the first 10 chapters cover the topic extensively. That reflectance distribution function you saw at the end of the semester? It's not an advanced topic. It's what realistic rendering is built from. How to represent it, evaluate it, and integrate it are the concerns of the next 10 chapters.

The hypothetical Ideal Graphics Professional has majored in Math, Computer Science, Physics, Perceptual Psychology, and Mechanical Engineering. No one has that background, but if you majored in any of these subjects and then patiently read this book, you will appreciate how the themes combine in a remarkable way whenever a pixel is drawn.

Volume 1:
This book is comprehensive in scope and one of the most well-written technical books in existence. In the preface the author states 'I love to write', and considering the exceptional quality of this book, this indeed shows through.

The first part of the book covers the human visual system, the understanding of which is fundamental to designing effective computer graphics. Several interesting topics are discussed, including Mach bands, color opponency, perceptual color matching, MacAdam ellipses, RGB color space, and gamut mapping.

The second part covers more technical matters, namely that of signal processing. The mathematical background assumed of the reader increases dramatically in this part; some exposure to elementary calculus and differential equations would suffice. The author does a good job of explaining such concepts as linear operators and the Dirac bracket notation. The pictorial representation he gives of the convolution operation is very helpful. In addition, Fourier analysis is presented at a level that makes it very clear exactly what is happening to signals, both discrete and continuous, when taking the Fourier transform. The Fast Fourier transform is not discussed however, dissapointingly. Suprisingly, a whole chapter is devoted to wavelet transforms, a topic usually not included at this level. Wavelets are used as a tool to deal with nonstationary signals. Usually discussed at a very abstract level, the presentation here is crystal clear and vey intutive, and the reader will take away a deeper appreciation of these objects than what could have been obtained from the usual presentations.

Chapter 7 is one of the most important in the book for it covers Monte Carlo techniques for evaluating the integrals that arise in image processing. The speed of convergance of Monte Carlo is addressed, along with how to estimate confidence levels when the parent distribution is normal. The author presents five different ways of doing 'blind' Monte Carlo, including rejection, blind stratified, weighted, and quasi Monte Carlo. Quasi Monte Carlo has taken on particular importance in recent years wherever Monte Carlo techniques are used. The author also presents four different ways of doing 'informed' Monte Carlo, i.e. when some information about the signal is known.

Uniform sampling of continuous signals is done in the next chapter. After discussing an example of sampling and reconstruction, the author outlines in detail the mathematical theory behind the uniform sampling and reconstruction of one-and two-dimensional signals. The chapter ends with a discussion of a technique to reduce aliasing artifacts called supersampling.

The next chapter covers nonuniform sampling and reconstruction. Naturally this is more complicated from a mathematical standpoint, due to the role of stochastic processes, but the author does a good job of discussing the relevant concepts. Most interesting is his treatment of the duality between aliasing and noise.

Chapter 10 surveys some of the more modern and practical techniques used for sampling and reconstruction of two-dimensional signals. Uniform sampling is discussed in terms of rectangular and hexagonal lattices; nonuniform sampling in terms of Poisson sampling and N-books sampling. Pseudocode is given for the decreasing radius algorithm. The concept of a refinement test is introduced and broken down into five categories, each of which is discussed in detail. The refinement test allows one to decide when more samples are needed in a neighborhood, and refinement geometry indicates where the samples are to be placed. Refinement geometry is discussed in this chapter also, with linear and area bisection techniques outlined, along with multiple-level and tree-based sampling. Techniques for interpolation and reconstruction, such as warping are also treated, and the author gives brief overviews of one-dimensional and two-dimensional sampling theorems. Numerous other methods, going by several different names are also discussed.

A very large set of references is given at the end of the book, covering a wide variety of topics in computer graphics and mathematical formalism. I have not read the second volume, but I am sure it respects the high quality of the first.

If you read the detailed table of contents for this book, which I provide below since it is not in the product description on this site, it can be quite intimidating. Don't let it be. If you know something about linear algebra at the level of just matrices and vectors and you are famililar with the basic ideas of calculus, even if it has been some time since you actually went through the steps of performing derivatives and integrals, then you can grasp the contents of this book. This is not a book about specific digital imaging tricks. The book sticks to the subject of digital image synthesis, which is converting the description of an image into an image itself. Even on that subject, don't expect a lot of algorithms in numbered steps. This is more of a book about the unified theory of digital image synthesis, showing how the knowledge of physics, numerical methods, signal processing, the human visual system, and even mechanical engineering (fluid flow in particular) all work together to give you insight into how to synthesize images. The only real complaint I have is the extensive errata necessary for reading the book. It is quite annoying to be plowing along, reading and trying to grasp a concept, when I come across an equation that just has to be wrong - and it usually is. The errata is online, however. I suggest you print it out and be prepared to use it often. Glassner is a wonderful writer, and his love and knowledge of the subject shows. For that reason it is worth putting up with the errors to get to the knowledge he has on this subject.

VOLUME I (UNITS I AND II)
I THE HUMAN VISUAL SYSTEM AND COLOR
1 The Human Visual System
1.1 Introduction
1.2 Structure and Optics of the Human Eye
1.3 Spectral and Temporal Aspects of the HVS
1.4 Visual Phenomena
1.4.1 Contrast Sensitivity
1.4.2 Noise
1.4.3 Mach Bands
1.4.4 Lightness Contrast and Constancy
1.5 Depth Perception
1.5.1 Oculomotor Depth
1.5.2 Binocular Depth
1.5.3 Monocular Depth
1.5.4 Motion Parallax
1.6 Color Opponency
1.7 Perceptual Color Matching:; CIE XYZ Space
1.8 Illusions
1.9 Further Reading
1.10 Exercises
2 Color Spaces
2.1 Perceptually Uniform Color Spaces: L*u*v* and L*a*b*
2.2 Other Color Systems
2.3 Further Reading
2.4 Exercises
3 Displays
3.1 Introduction
3.2 CRT Displays
3.3 Display Spot Interaction
3.3.1 Display Spot Profile
3.3.2 Two-Spot Interaction
3.3.3 Display Measurement
3.3.4 Pattern Description
3.3.5 The Uniform Black Field (t = 0)
3.3.6 Clusters of Four (t = .25)
3.3.7 Clusters of Two (t = .5)
3.3.8 The Uniform White Field (t = 1)
3.3.9 Spot Interaction Discussion
3.4 Monitors
3.5 RGB Color Space
3.5.1 Convertin XYZ to Spectra
3.6 Gamut Mapping
3.7 Further Reading
3.8 Exercises

II SIGNAL PROCESSING
4 Signals and Systems
4.1 Introduction
4.2 Types of Signals and Systems
4.2.1 Continuous-Time (CT) Signals
4.2.2 Discrete-Time (DT) Signals
4.2.3 Periodic Signals
4.2.4 Linear Time-Invariant Systems
4.3 Notation
4.3.1 The Real Numbers
4.3.2 The Integers
4.3.3 Intervals
4.3.4 Product Spaces
4.3.5 The Complex Numbers
4.3.6 Assignment and Equality
4.3.7 Summation and Integration
4.3.8 The Complex Exponentials
4.3.9 Braket Notation
4.3.10 Spaces
4.4 Some Useful Signals
4.4.1 The Impulse Signal
4.4.2 The Box Signal
4.4.3 The Impulse Train
4.4.4 The Sinc Signal
4.5 Convolution
4.5.1 A Physical Example of Convulution
4.5.2 The Response of Composite Systems
4.5.3 Eigenfuctions and Frequency Response of LTI Systems
4.5.4 Discrete-Time Convolution
4.6 Two-Dimensional Impulse Response
4.6.1 Linear Systems
4.6.2 Two-Dimensional Impulse Response
4.6.3 Convolution
4.6.4 Two-Dimensional Impulse Response
4.6.5 Eigenfunctions and Frequency Response
4.7 Further Reading
4.8 Exercises
5 Fourier Transforms
5.1 Introduction
5.2 Basis Functions
5.2.1 Projections of Points in Space
5.2.2 Projection of Functions
5.2.3 Orthogonal Families of Functions
5.2.4 The Dual Basis
5.2.5 The Complex Exponential Basis
5.3 Representation in Bases of Lower Dimension
5.4 Continuous-Time Fourier Representations
5.5 The Fourier Series
5.5.1 Convergence
5.6 The Continuous-Time Fouier Transform
5.6.1 Fourier Transform of Periodic Signals
5.6.2 Parseval's Theorem
5.7 Examples
5.7.1 The Box Signal
5.7.2 The Box Specturm
5.7.3 The Guassian
5.7.4 The Impulse Signal
5.7.5 The Impulse Train
5.8 Duality
5.9 Filtering and Convolution
5.9.1 Some Common Filters
5.10 The Fourier Transform Table
5.11 Discrete-Time Fourier Represetnations
5.11.1 The Discrete-Time Fourier Series
5.11.2 The Discrete-Time Fourier Transform
5.12 Fourier Series and Transforms Summary
5.13 Convolution Revisited
5.14 Two-Dimensional Fourier Transforms
5.14.1 Continuous-Time 2D Fourier Transforms
5.14.2 Discrete-Time 2D Fourier Transforms
5.15 Higher-Order Transforms
5.16 The Fast Fourier Transform
5.17 Further Reading
5.18 Exercises
6 Wavelet Transforms
6.1 Introduction
6.2 Short-Time Fourier Transform
6.3 Scale and Resolution
6.4 The Dilation Equation and the Haar Transform
6.5 Decomposition and Reconstruction
6.5.1 Building the Operators
6.6 Compression
6.7 Coefficient Conditions
6.8 Multiresolution Analysis
6.9 Wavelets in the Fourier Domain
6.10 Two-Dimensional Wavelets
6.10.1 The Rectangular Wavelet Decomposition
6.10.2 The Square Wavelet Decomposition
6.11 Further Reading
6.12 Exercises
7 Monte Carlo Integration
7.1 Introduction
7.2 Baisc Monte Carlo Ideas
7.3 Confidence
7.4 Blind Monte Carlo
7.4.1 Crude Monte Carlo
7.4.2 Rejection Monte Carlo
7.4.3 Blind Stratified Sampling
7.4.4 Quasi Monte Carlo
7.4.5 Weighted Monte Carlo
7.4.6 Multidimensional Weighted Monte Carlo
7.5 Informed Monte Carlo
7.5.1 Informed Stratified Sampling
7.5.2 Importance Sampling
7.5.3 Control Variates
7.5.4 Antithetic Variates
7.6 Adaptive Sampling
7.7 Other Approaches
7.8 Summary
7.9 Further Reading
7.10 Exercises
8 Uniform Sampling and Reconstruction
8.1 Introduction
8.1.1 Sampling: Anti-Aliasing in a Pixel
8.1.2 Reconstruction: Evaluating Incident Light at a Point
8.1.3 Outline of this Chapter
8.1.4 Uniform Sampling and Reconstruction of a 1D Continuous Signal
8.1.5 What Signal are Bandlimited?
8.2 Reconstruction
8.2.1 Zero-Order Hold Reconstruction
8.3 Sampling in Two Dimensions
8.4 Two-Dimensional Reconstruction
8.5 Reconstruction in Image Space
8.5.1 The Box Reconstruction Filter
8.5.2 Other Reconstruction Filters
8.6 Supersampling
8.7 Further Reading
8.8 Exercises
9 Nonuniform Sampling and Reconstruction
9.1 Introduction
9.1.1 Variable Sampling Density
9.1.2 Trading Aliasing for Noise
9.1.3 Summary
9.2 Nonuniform Sampling
9.2.1 Adaptive Sampling
9.2.2 Aperiodic Sampling
9.2.3 Sampling Pattern Comparison
9.3 Informed Sampling
9.4 Stratified Sampling
9.4.1 Importance Sampling
9.4.2 Importance and Stratified Sampling
9.5 Interlude: The Duality of Aliasing and Noise
9.6 Nonuniform Reconstruction
9.7 Further REAding
9.8 Exercises
10 Sampling and Reconstruction Techniques
10.1 Introduction
10.2 General Outline of Signal Estimation n
10.3 Initial Sampling Patterns
10.4 Uniform and Nonuniform Sampling
10.5 Initial Sampling
10.5.1 Uniform Sampling
10.5.2 Rectangular Lattice
10.5.3 Hexagonal Lattice
10.5.4 Triangular Lattice
10.5.5 Diamond Lattice
10.5.6 Comparison of Subdivided Hexagonal and Square Lattices
10.5.7 Nonuniform Sampling
10.5.8 Poisson Sampling
10.5.9 N-Rooks Sampling
10.5.10 Jitter Distribution
10.5.11 Poisson-Disk Pattern
10.5.12 Precomputed Poisson-Disk Patterns
10.5.13 Multiple-Scale Poisson-disk Patterns
10.5.14 Sampling Tiles
10.5.15 Dynamic Poisson-Disk Patterns
10.5.16 Importance Sampling
10.5.17 Multidimensional Patterns
10.5.18 Discussion
10.6 Refinement
10.6.1 Sample Intensity
10.7 Refinement Tests
10.7.1 Intensity Comparison Refinement Test
10.7.2 Contrast Refinement Test
10.7.3 Object-Based Refinement Test
10.7.4 Ray-Tree Comparison Refinement Test
10.7.5 Intensity Statistics Refinement Test
10.8 Refinement Sample Geometry
10.9 Refinement Geometry
10.9.1 Linear Bisection
10.9.2 Area Bisection
10.9.3 Nonuniform Geometry
10.9.4 Multiple-Level Sampling
10.9.5 Tree-Based Sampling
10.9.6 Multiple-Scale Template Refinement
10.10 Interpolation and Recontruction
10.10.1 Functional Techniques
10.10.2 Warping
10.10.3 Piecewise-Continuous Recontruction
10.10.5 Local Filtering
10.10.6 Yen's Method
10.10.7 Multistep Reconstruction
10.11 Further Reading
10.12 Exercises Bibiography
Index

VOLUME II (UNITS III, IV, AND V)
III MATTER AND ENERGY
11 Light
11.1 Introduction
11.2 The Double-Slit Experiment
11.3 The Wave Nature of Light
11.4 Polarization
11.5 The Photoelectric Effect
11.6 Particle-Wave Duality
11.7 Reflection and Transmission
11.8 Index of Refraction
11.8.1 Sellmeier's Formula
11.8.2 Cauchy's Formula
11.9 Computing Specular Vectors
11.9.1 The Reflected Vector
11.9.2 Total Internal Reflection
11.9.3 Transmitted Vector
11.10 Further Reading
11.11 Exercises
12 Energy Transport
12.1 Introduction
12.2 The Rod Model
12.3 Particle Density and Flux
12.4 Scattering
12.4.1 Counting New Particles
12.5 The Scattering-Only Particle Distribution Equations
12.6 A More Complete Medium
12.6.1 Explicit Flux
12.6.2 Implicit Flux
12.7 Particle Transport in 3D
12.7.1 Points
12.7.2 Projected Areas
12.7.3 Directions
12.7.4 Solid Angles
12.7.5 Integrating over solid Angles
12.7.6 Direction Sets
12.7.7 Particles
12.7.8 Flux
12.8 Scattering in 3D
12.9 Components of 3D Transport
12.9.1 Streaming
12.9.2 Emission
12.9.3 Absorption
12.9.4 Outscattering
12.9.5 Inscattering
12.9.6 A Complete Transport Model
12.9.7 Isotropic Materials
12.10 Boundary Conditions
12.11 The Integral Form
12.11.1 An Example
12.11.2 The Integral Form of the Transport Equation
12.12 The Light Transport Equation
12.13 Further Reading
12.14 Exercises
13 Radiometry
13.1 Introduction
13.2 Radiometric Conventions
13.3 Notation
13.4 Spherical Patches
13.5 Radiometric Terms
13.6 Radiometric Relations
13.6.1 Discussion of Radiance
13.6.2 Spectral Radiometry
13.6.3 Photometry
13.7 Reflectance
13.7.1 The BRDF fr
13.7.2 Reflectance p
13.7.3 Reflectance Factor R
13.8 Examples
13.8.1 Perfect Diffuse
13.8.2 Perfect Specular
13.9 Spherical Harmonics
13.10 Further Reading
13.11 Exercises
14 Materials
14.1 Introduction
14.2 Atomic Structure
14.3 Particle Statistics
14.3.1 Fermi-Dirac Statistics
14.4 Molecular Structure
14.4.1 Ionic Bonds
14.4.2 Molecular-Orbital Bonds
14.5 Radiation
14.6 Blackbodies
14.6.1 Bose-Einstein Statistics
14.7 Blackbody Energy Distribution
14.7.1 Constant Index of Refraction
14.7.2 Linear Index of Refraction
14.7.3 Radiators
14.8 Phosphors
14.9 Further Reading
14.10 Exercises
15 Shading
15.1 Introduction
15.2 Lambert, Phong, and Blinn-Phong Shading Models
15.2.1 Diffuse Plus Specular
15.3 Cook-Torrance Shading Models
15.3.1 Torrance-Sparrow Microfacets
15.3.2 Fresnel's Formulas
15.3.3 Roughness
15.3.4 The Cook-Torrance Model
15.3.5 Polarization
15.4 Anistropy
15.4.1 The Kajiya Model
15.4.2 The Poulin-Fournier Model
15.5 The HTSG Model
15.6 Empirical Models
15.6.1 The Strauss Model
15.6.2 The Ward Model
15.6.3 The Programmable Model
15.7 Precomputed BRDF
15.7.1 Sampled Hemispheres
15.7.2 Spherical Harmonics
15.8 Volume Shading
15.8.1 Phase Functions
15.8.2 Atmospheric Modeling
15.8.3 The Earth's Ocean
15.8.4 The Kubelka-Munk Pigment Model
15.8.5 The Hanrahan-Krueger Multiple-Layer Model
15.9 Texture
15.10 Hierarchies of Scale
15.11 Color
15.12 Further Reading
15.13 Exercises
16 Integral Equations
16.1 Introduction
16.2 Types of Integral Equations
16.3 Operators
16.3.1 Operator Norms
16.4 Solution Techniques
16.4.1 Residual Minimization
16.5 Degenerate Kernels
16.6 Symbolic Methods
16.6.1 The Fubini Theorem
16.6.2 Successive Substitution
16.6.3 Neumann Series
16.7 Numerical Approximations
16.7.1 Numerical Integration (Quadrature)
16.7.2 Method of Undetermined Coefficients
16.7.3 Quadrature on Expanded Functions
16.7.4 Nystrom Method
16.7.5 Monte Carlo Quadrature
16.8 Projection Methods
16.8.1 Projection
16.8.2 Pictures of the Function Space
16.8.3 Polynomial Collocation
16.8.4 Tchebyshev Approximation
16.8.5 Least Squares
16.8.6 Galerkin
16.8.7 Wavelets
16.8.8 Discussion
16.9 Monte Carlo Estimation
16.9.1 Random Walks
16.9.2 Path Tracing
16.9.3 The Importance Function
16.10 Singularities
16.10.1 Removal
16.10.2 Factorization
16.10.3 Divide and Conquer
16.10.4 Coexistence
16.11 Further Reading
16.12 Exercises
17 The Radiance Equation
17.1 Introduction
17.2 Forming the Radiance Equation
17.2.1 BDF
17.2.2 Phosphorescence
17.2.3 Fluorescence
17.2.4 FRE
17.3 TIGRE
17.4 VTIGRE
17.5 Solving for L
17.6 Further Reading
17.7 Exercises

IV RENDERING
18 Radiosity
18.1 Introduction
18.2 Classical Radiosity
18.2.1 Collocation Solution
18.2.2 Galerkin Solution
18.2.3 Classical Radiosity Solution
18.2.4 Higher-Order Radiosity
18.3 Solving the Matrix Equation
18.3.1 Jacobi Iteration
18.3.2 Gauss-Seidel Iteration
18.3.3 Southwell Iteration
18.3.4 Overrelaxation
18.4 Solving Radiosity Matrices
18.4.1 Jacobi Iteration
18.4.2 Gauss-Seidel Iteration
18.4.3 Southwell Iteration
18.4.4 Progressive Refinement
18.4.5 Overrelaxation
18.4.6 Comparison
18.5 Form Factors
18.5.1 Analytic Methods
18.5.2 Contour Integration
18.5.3 Physical Devices
18.5.4 Projection
18.5.5 Discussion
18.6 Hierarchical Radiosity
18.6.1 One Step of HR
18.6.2 Adaptive HR
18.6.3 Importance HR
18.6.4 Discussion
18.7 Meshing
18.8 Shooting Power
18.9 Extensions to Classical Radiosity
18.10 Further Reading
18.11 Exercises
19 Ray Tracing
19.1 Introduction
19.2 Photon and Visibility Tracing
19.3 Visibility Tracing
19.3.1 Strata Sets
19.3.2 Applying Resolved Strata
19.3.3 Direct and Indirect Illumination
19.3.4 Discussion
19.4 Photon Tracing
19.5 Bidirectional Ray-Tracing Methods
19.6 Hybrid Algorithms
19.7 Ray-Tracing Volumes
19.8 Further Reading
19.9 Exercises
20 Rendering and Images
20.1 Introduction
20.2 Postprocessing
20.2.1 A Nonlinear Observer Model
20.2.2 Image-Based Processing
20.2.3 Linear Processing
20.3 Feedback Rendering
20.3.1 Illumination Painting
20.3.2 Subjective Constraints
20.3.3 Device-Directed Rendering
20.4 Further Reading
20.5 Exercise
21 The Future
21.1 Technical Progress
21.1.1 Physical Optics
21.1.2 Volume Rendering
21.1.3 Information Theory
21.1.4 Beyond Photo-Realism: Subjective Rendering
21.2 Other Directions
21.3 Summary

V APPENDICES
A Linear Algebra
A.1 General Notation
A.2 Linear Spaces
A.2.1 Norms
A.2.2 Inf and Sup
A.2.3 Metrics
A.2.4 Completeness
A.2.5 Inner Products
A.3 Function Spaces
A.4 Further Reading
B Probability
B.1 Events and Probability
B.2 Total Probability
B.3 Repeated Trials
B.4 Random Variables
B.5 Measures
B.6 Distributions
B.7 Geometric Series
B.8 Further Reading
C Historical Notes
C.1 Specular Reflection and Transmission
C.1.1 Specular Reflection
C.1.2 Specular Transmission
D Analytic Form Factors
D.1 Differential and Finite Surfaces
D.1.1 Differential to Differential
D.1.2 Differential to Finite
D.1.3 Finite to Finite
D.2 Two Polygons
E Constants and Units
F Luminaire Standards
F.1 Terminology
F.2 Notation
F.3 The IES Standard
F.3.1 The Big Picture
F.3.2 The Tilt Block
F.3.3 The Photometry Block
F.4 The CIE Standard
F.4.1 The Main Block
F.4.2 The Measurement Block
F.4.3 The Photometry Block
G Reference Data
G.1 Material Data
G.2 Human Data
G.3 Light Sources
G.4 Phosphors
G.5 Macbeth ColorChecker
G.6 Real Objects

This is the one really fundamental book on rendering computer images. Watt's book is an excellent introduction to many basic principles, but Glassner's volumes are an advanced text suitable even for experienced graphics professionals. Here you can find a description of fourier analysis and wavelets using the same notation, a survey of appearance science, and a good description of the physics underlying rendering algorithms. It really has everything. There are some typos in some of the equations, but the errata are available online. I find myself using these volumes all the time.

This set of books is a unique & authorative resource guiding you though a whole range of 2D and 3D graphics concepts and algorithms. I have referred to it many times for explainations and definitions related to 3D rendering and shading models, among other things. However, this is NOT just an encyclopedia or compilation of other people's works - the author (a respected senior CG researcher himself) has gone through a whole body of knowledge, explaining and linking each concept, and including the CG algorithms in a broader context along with concepts of human perception and underlying theories. Nothing like this has ever been written - I would recommend it to anyone who can understand it, with the sole warning that some college-level math (at least a little calculus) is needed in order to read and follow much of what he covers.

This book is the first serious work to bring together elements from disparate areas of science, such as optics, psychology, and monte carlo sampling, and show how these are used in the field of computer graphics. It is not a book about how to code various algorithms, rather it is a place to go to learn about what has been done in the field and to gain inspiration for further research. It is for the most part readable, but not light reading. These two volumes are a great time saver if you need to understand the relevant theory in a particular area of research; the author has combed through literally thousands of papers and books and presented the essence of many of the best of them. Its high rating can be justified by the fact that it is the only work of its kind; that it is well illustrated, informative, and useful makes it all the better.

Among all of the CG books on my shelf, this is the only one which binds the CG subjects to physics origin so deeply. Glassner prepared a very nice collection of reference information, explained the historical reasons of several confusing stuff in CG.

It's true that it has number of mistakes / typos but there's an online errata .... Once you check and note down the errata in the proper places of the book - which may take your 1hr at the most-, nothing will remain to complain about this book.

If you are serious about CG, you'll love the information in this book. It's a bit expensive but surely worth the price.

I was expecting much more out of this 2 volume set. The books are full of mistakes, especially in formulas. If you plan on purchasing these books, make sure to download and print out the errata as well. It could save many headaches in trying to understand formulas that don't agree with the accompanying explanation. I don't doubt that Glassner is a very intelligent man, but his descriptions are somewhat dense and difficult to decypher sometimes. My recommendation: get an ACM SIGGRAPH membership to get access to many of the papers in this field and get the algorithms straight from the source.

I was quite disappointed with the number of mistakes in this book as I would have liked to have used it as a reference.

Many sections were grabbed from research papers which I am familiar with and no attempt was made to keep notations consistent between papers. This meant that as you are reading the notations will suddenly switch on you.

I think the undertaking was too ambitious although the topics are quite pertinent.

Perhaps the next release will address these problems.

I have not read this book throughly yet, but I have long
been hoping get this book to read and finally I get it.
It must be a good fundamental image synthesis book because
I remember once some graphics guru recommend this book
to me strongly, but forget when and who:)