Could Nano-Camera Work at the Speed of Light?

A recent study looks at a three-dimentional camera that was presented last week at the Siggraphy Asian in Hong Kong, which many believe could be used in medical imaging to provide collision avoidance detectors for cars via interactive video gaming devices. 

The camera works via a "time for flight" technology that's used in Microsoft's recently launched second generation from the kinetic device that works to calculate the amoutn of time it takes to signal a reflection. 

"Using the current state of the art, such as the new Kinect, you cannot capture translucent objects in 3-D," Kadambi said, via a press release. "That is because the light that bounces off the transparent object and the background smear into one pixel on the camera. Using our technique you can generate 3-D models of translucent or near-transparent objects."

Researchers demonstated that once the light signal has been fired, it boucnes off an object and can be easily seen via camera in order to calculate the distance that the signal has traveled and the depth of the object it has reflected from. 

However, due to changing environments, including semitransparent surfaces, edges and motions, the findings show correct measurements that can be rather difficult to track. 
Fortunately, the study notes that this new device works on an encoding technique that's used in the telecommunications industry and calculates the distance of a signal that's traveled. 

"We use a new method that allows us to encode information in time," Ramesh Raskar, an associate professor of media arts and sciences and leader of the camera culture group within the media lab said, via the release. "So when the data comes back, we can do calculations that are very common in the telecommunications world, to estimate different distances from the single signal."

The idea is similar to existing techniques that clear blurring in photographs, says Bhandari, a graduate student in the Media Lab. "People with shaky hands tend to take blurry photographs with their cellphones because several shifted versions of the scene smear together," Bhandari said, via the release. "By placing some assumptions on the model - for example that much of this blurring was caused by a jittery hand - the image can be unsmeared to produce a sharper picture."

More information regarding the study can be found here.