The Magic of Computer Graphics: Landmarks in Rendering

The Magic of Computer Graphics: Landmarks in Rendering

Noriko Kurachi, Michael Stark

Language: English

Pages: 428

ISBN: 2:00256357

Format: PDF / Kindle (mobi) / ePub


Author note: Edited by Michael Stark
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Computer graphics is a vast field, and getting larger every day. It is impossible to cover every topic of interest, even within a specialization such as CG rendering. For many years, Noriko Kurachi has reported on the latest developments for Japanese readers in her monthly column for CG World. Being something of a pioneer herself, she selected topics that represented original and promising new directions for research, as opposed to the tried and true methods.

Many of these novel ideas paid off handsomely, and these are the topics covered in this book. Starting from the basic behavior of light, Ms. Kurachi introduces the most useful techniques for global and local illumination using geometric descriptions of an environment in the first section. She then goes on to describe image-based techniques that rely on captured data to do their magic in the second section. In the final section, she looks at the synthesis of these two complementary approaches and what they mean for the future of computer graphics.

"The book you hold today tells the story of this new era of computer graphics. Working closely with researchers who helped lead this revolution, Noriko Kurachi describes these key innovations and brings them together as a coherent body of knowledge. Please read this book, practice the techniques, and figure out if they will allow you to create the visions you have in your mind." -Paul Debevec, pioneer in HDR imaging and image-based modeling

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causes the daytime sky to 53 54 3. Volume Rendering and Participating Media appear blue. In fact, the appearance of some objects, such as clouds and smoke, comes from the way light is affected as it passes through them. Such objects are really volume objects and do not even have well-defined surface boundaries. Even when surfaces are well defined, BRDF-based rendering is limited in that it does not account for the diffuse transmission of light exhibited by translucent objects, nor does it

of Japan it is uncommon to find a book with contents like this one. I became convinced that it would be meaningful if this book were translated into English and could be read by a wider range of people. Therefore, as soon as the Japanese book was released, I started translating it into English, and when half of translation was completed, I told Greg Ward, who had always been supportive of my writing the book, about my plan of releasing an English edition. He was very positive and gave me the

method used for real-time rendering of volume data. This may seem surprising, because ray tracing has a reputation of being too slow for real-time rendering. However, ray tracing is slow for surface rendering because of the cost of computing ray intersections. Ray/voxel intersections are much simpler because of the regular placement of the voxels and the simplicity of intersecting a cube with a ray. 3.2.2 History of Volume Rendering The emergence of computed tomography (CT) in the 1970s was a

values to pixel values is modeled as a function of the pixel exposure. This function (or its graph) is known as the response curve of the device (Figure 6.1). The discussion here refers to digital cameras, but the same principles apply to film cameras. The difference is that film does not have discrete pixels. Exposure at a point refers to the irradiance on a small neighborhood of a point on the film. The response curve of a film camera is really the response curve of the particular film with

g and the irradiance E, the other unknown. Because the pixel values are discrete, the inverse function g has a finite domain running over the possible integer pixel values Zmin , . . . , Zmax (the process is 169 6.1. Response Curves and HDR Imaging repeated for each color channel). Consequently, g is entirely determined by its value on an array of integer pixel values. Replacing g(Z) with the array value g[Z] and applying Equation (6.3) to each pixel sample Zi j , g[Zi j ] = ln Ei + ln Δt j

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