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Stereo Prism

The polyoptic wheel's third and final setting allows Bigshot to capture 3D (or stereo) photos.   The science behind capturing stereo pictures is inspired by the way our own eyes work.   In 1838, Charles Wheatstone, a British scientist, discovered that the horizontal separation between our two eyes is what allows us to perceive depth [1]. One eye sees what the other eye sees from a slightly shifted perspective.  The brain uses both of the perspectives to estimate the depth of the objects in the scene.

The basic idea behind stereoscopy is to trick the brain into perceiving depth by presenting each eye with an image of the same scene.  The trick is that the images form a stereo pair, which means they are taken at slightly shifted perspectives from one another.  An anaglyph image [2] is a special kind of stereo pair where the red component of the first image is placed on top of the blue and green components of the second image.  Since the components are slightly shifted from each other, anaglyph images look like two misaligned pieces when viewed normally.  By wearing red-cyan tinted 3D glasses, each eye is prevented from seeing one of the pieces, and the anaglyph image appears three-dimensional.

Stereo Prism
Figure 6: 3D Stereo image formation with Prism

Figure 6 shows Bigshot's biprism optical module, which is used to capture a stereo pair.   The biprism splits the field of view into two halves, shown by the red and blue rays in Figure 6, and both halves are captured by the image sensor.   By using the biprism in this manner, Bigshot is effectively capturing two images of the same scene at once, just as a pair of side-by-side cameras could [3].

References
[1] "Charles Wheatstone," Oct 2, 2009. [Online]. Available: http://en.wikipedia.org/wiki/Charles_Wheatstone. [Accessed: Oct 4, 2009].
[2] "Anaglyph Image," Oct 2, 2009. [Online]. Available: http://en.wikipedia.org/wiki/Anaglyph_image. [Accessed: Oct 4, 2009].
[3] D-H. Lee, I. S. Kweon, and R. Cipolla. A Biprism-Stereo Camera System. IEEE Conference on Computer Vision and Pattern Recognition, Volume 1, June 1999.

 

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