Harvard SEAS Advances Digital Imaging
Cambridge, MA -- Computer graphics and digital video lag behind reality; despite advances, the best software and video cameras still cannot seem to get computer-generated images and digital film to look exactly the way our eyes expect them to.
But Hanspeter Pfister and Todd Zickler, computer science faculty at the Harvard School of Engineering and Applied Sciences (SEAS), are working to narrow the gap between ‘virtual’ and ‘real’ by asking a common question: how do we see what we see?
Between them, Pfister and Zickler are presenting three papers this week at SIGGRAPH 2013, the 40th International Conference and Exhibition on Computer Graphics and Interactive Techniques.
One project led by Zickler, the William and Ami Kuan Danoff Professor of Electrical Engineering and Computer Science, tries to find better ways to mimic the appearance of a translucent object, such as a bar of soap. The paper elucidates how humans perceive and recognize real objects and how software can exploit the details of that process to make the most realistic computer-rendered images possible.
“If I put a block of butter and a block of cheese in front of you, and they’re the same color, and you’re looking for something to put on your bread, you know which is which,” says Zickler. “The question is, how do you know that? What in the image is telling you something about the material?”
His hope is to eventually understand these properties well enough to instruct a computer with a camera to identify what material an object is made of and to know how to properly handle it—how much it weighs or how much pressure to safely apply to it—the way humans do.
The new approach focuses on translucent materials’ phase function, part of a mathematical description of how light refracts or reflects inside an object, and, therefore, how we see what we see.
In the past, phase function shape was considered relevant to an object's translucent appearance, but formal perceptual studies had never been carried out. This is because the space of different phase functions is so vast and perceptually diverse to the human brain that modern computational tools were required to generate and analyze so many different images.
Zickler’s team took advantage of increased computational power to trim down the potential space of images to a manageable size. They first rendered thousands of computer-generated images of one object with different computer-simulated phase functions, so each image’s translucency was slightly different from the next. From there, a program compared each image’s pixel colors and brightness to another image in the space and decided how different the two images were. Through this process, the software created a map of the phase function space according to the relative differences of image pairs, making it easy for the researchers to identify a much smaller set of images and phase functions that were representative of the whole space.
Finally, the researchers asked people to compare these representative images and judge how similar or different they were, shedding light on the properties that help us decide which objects are plastic and which are soap simply by looking at them.