Imagination Amplification

54 January/February 2000 Michael F. Cohen Microsoft Research It would be easy to gaze into the distant future and speculate what image synthesis technology may be. Since Star Trek with its Holodeck has already done this far better than I can, prognosticating will not be my main focus here. Instead, I hope to relate some of my own inspiration for getting involved with computer graphics, an inspiration that is still an unrealized dream, one that I’m uncertain how to fully achieve. My interest in computer graphics can best be summed up as a desire to create tools for imagination amplification. I suspect I did not invent the term imagination amplification, but I don’t remember the source. We all have thoughts about things that don’t exist. These may range from simple musings, to architectural designs, to industrial design problems. We all have drawn on the backs of envelopes or napkins to try to convey a sense of our thought process. Drawing not only communicates, it also helps to clarify thinking. This process of creating images from ideas I call imagination amplification. Computers can help. The computer can, in the form of an algorithm, be given information about how light reflects from objects, how materials bend and bounce, how ink flows on a piece of paper, how cameras create images from light, and more. Thus, from only partial information, the computer can simulate (to some approximation) the physics of materials, paint, and light to generate images we couldn’t create on our own. The computer can show us how things will change when the input changes. Realistic image synthesis has made tremendous progress over the past three decades. The first major body of work on raster images of synthetic objects came from the University of Utah in the 1970s. To understand just how indebted we are to this time and place in history, see http://www.cs.utah.edu/dept/history/. If the 1970s can be considered the decade of local illumination, the 1980s were the decade of global illumination. Ray tracing and radiosity algorithms provide the ability to capture the subtleties of interreflection and shadows. Figure 1 (left) shows a radiosity image I helped create 10 or so years ago at Cornell University. Recent improvements have resulted in images such as Figure 1 (right), a Florence Courtyard. Since their inception, ray tracing and radiosity methods have not changed fundamentally in their basic algorithm, although improvements in the 1990’s have made them much faster and more widely applicable. Radiosity methods have given way to radiance methods that also capture nondiffuse interreflection. Stochastic and hybrid methods have brought together the best of both families of algorithms. These changes, along with the steady increase in computational power, bring global illumination methods to a point where they could be used on a wide scale. However, they aren’t.