The Magic of Computer Graphics

Author: Noriko Kurachi
Publisher: CRC Press, 2011
Pages: 448
ISBN: 978-1568815770
Aimed at: Academic computer scientists who already know basics of computer graphics
Rating: 5
Pros: Covers topics neglected elsewhere; a mathematical approach
Cons: No introductory level content; mathematical approach
Reviewed by: Mike James

The biggest problem with this book is its title - "The Magic" suggests that it is going to be light and fluffy  but no. This is an expert's book and it is full of mathematics. It won't even suit all experts but if you are interested in computer graphics then it has to be on your bookshelf - let me explain why.

The emphasis of this book is on photorealistic rendering of models and closely related techniques. It doesn't contemplate how you get the model and it is assumed that it is a problem solved and discussed elsewhere. It is also very much concerned with photorealism and as such it goes to great lengths to understand the physics of how light interacts with real world objects.

The book started life as a regular technical column in a Japanese magazine on computer graphics which tended to pick on topics that had been overlooked or hadn't been widely discussed. This is still true of this book, and if you are expecting a standard textbook on computer graphics you need to think again. This is a fairly idiosyncratic selection of topics and you probably need to know the basics of 3D graphics and rendering in particular.

 

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Part I of the book is the closest to traditional computer graphics in that it looks at the geometric approach - mainly via ray tracing and related techniques. It starts off with an in-depth look at the physics of light and how it interacts with real object to create a physical rendering. From there we move on to the rendering equation and its solutions - and if you aren't happy with integral equations you won't be happy with this book. Having said this, it is important to note that nothing is presented in an overly-complicated way. The material my be difficult, but this is your best chance of understanding it. After going over various ray tracing methods the chapter ends with a look at modern GPU-based hardware and ray tracing algorithms.

Chapter 3 is about volume rendering and it gives some examples of scientific imaging. Chapter 4 is even more specialized and looks at subsurface scattering with particular reference to rendering skin. This brings the geometric approach to a close. 

The next section is all about image-based approaches and many of the topics described are often not covered in mainstream computer graphics. The three chapters cover stereo imaging, HDR and image based lighting. The sorts of ideas covered are relevant to techniques for turning photos into virtual walkthroughs and the creation of "in-between frames" in animation. It includes a detailed explanation of the light field approach to plenoptic reconstruction. The chapter on image based lighting is about working out where the illumination sources are from a, usually HDR, image. The reason for wanting to do this is so as to be able to render a model to mix in correctly with the images.

The third section is on mixing geometric and image based approaches. The three chapters are on reconstruction of reflectance, bidirectional texture functions and radiance transfer.  If you can obtain reflectance function for an object, from a photo say,  you can improve the way traditional rendering works. The texture chapter follows the same course with a lot of analysis of textures aimed at making synthesis more accurate.

Don't buy this book if you need a basic course on graphics and don't buy it if you just want to find out about shaders or hardware. Certainly don't buy it if you are afraid of mathematics. If, however, you can cope with the math and you want to explore some topics in computer graphics which are the subject of current research then this is a really good book. As long as you are the right reader this is the right book.

 

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Mathematics for Machine Learning

Authors: Marc Peter Deisenroth, Aldo Faisal and Cheng Soon Ong
Publisher: Cambridge University Press
Pages: 398
ISBN: 978-1108455145
Print: 110845514X
Kindle: B083M7DBP6
Audience: Developers interested in machine learning
Rating: 3.5
Reviewer: Mike James
Lots of people need to learn the math behind mach [ ... ]



Deep Learning with JavaScript

Authors: Shanqing Cai, Stan Bileschi and Eric Nielsen
Publisher: Manning
Date: February 2020
Pages: 560
ISBN: 978-1617296178
Print: 1617296171
Audience: JavaScript Programmers
Rating: 5
Reviewer: Mike James
JavaScript doesn't seen a natural for AI but...


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Last Updated ( Saturday, 19 July 2014 )