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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importance function. The BRDF is largest where the reflection is most significant, so it is a natural choice for an importance function in GI integration. However, the BRDF says nothing about the directions of the strongest incoming light. Photon simulation can be used to precompute the dominant light directions. When this information is used in conjunction with the BRDF, better sampling can be achieved. Figure 2.8 illustrates such an approach. In the figure, the hemisphere is divided into

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Here σt = σs + σa , σs = (1 − g)σs . The value σs is known as the reduced scattering coefficient; g is the “scattering anisotropy” from Equation (3.3.3). As g approaches 1 the scattering has a stronger forward bias, which has less influence on the net flux. Combining Equations (4.3) and (4.5) produces ∇2 φ (x) = 3σa σt φ (x) − 3σt Q0 (x) + ∇ · Q1(x), (4.6) which is one form of the diffusion equation. The diffusion equation given in Equation (4.6) describes how particles are diffused within a

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