In this paper, we propose a method for high efficiency acquisition of Ray-Space for FTV (Free viewpoint TV). In this
research, incomplete data is directly captured by a novel device, i.e. photodiode/lens array, and transformed to full
information by Radon transform. We must capture the large amount of data in conventional acquisition of Ray-Space
using multiple cameras. However Ray-space has redundancy because it consists of set of lines which depend on depth of
objects. We use the Radon transform to exploit this redundancy. The Radon transform is set of projection data along
different directions. Thus Ray-space can be reconstructed from projection data in limited range by the inverse Radon
transform. Capturing the part of projection data correspond to capturing sums of several rays by 1 pixel. We have
simulated reconstruction of Ray-space projection data which was computed by computer simulation of capturing device.
As a result, by using fewer pixels than rays, we could reduce the information to reconstruct Ray-space.
In this paper, we propose a method for compressive acquisition of Ray-Space. Briefly speaking, incomplete data which
directly captured by a specific device is transformed to full information by Radon transform. Ray-Space, which
represents 3D images, describes position and direction of rays on reference plane in real space. Ray-Space has
information of many rays. In conventional acquisition of Ray-Space, multiple cameras are used and 1 pixel on a camera
captures 1 ray. Thus we need many pixels and we must capture the large amount of data. However Ray-Space has
redundancy because Ray-Space consists of set of lines which depend on the depth of objects. We use the Radon
transform to exploit this redundancy. The Radon transform is set of projection data along different directions. The Radon
transform of Ray-Space show uneven distribution. Thus Ray-Space can be reconstructed from projection data in limited
range by the inverse Radon transform. Capturing the part of projection data correspond to capturing sums of several rays
by 1 pixel. A sum of several rays means a sum of brightness of rays. In this paper, we have simulated reconstruction of
Ray-Space projection data which was computed by the Radon Transform of Ray-Space. This experiment showed that
Ray-Space could be reconstructed from the parts of projection data. As a result, using fewer pixels than rays, we could
reduce the amount of data to reconstruct Ray-Space.
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