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27 January 2011 Introducing the depth transfer curve for 3D capture system characterization
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Abstract
3D technology has recently made a transition from movie theaters to consumer electronic devices such as 3D cameras and camcorders. In addition to what 2D imaging conveys, 3D content also contains information regarding the scene depth. Scene depth is simulated through the strongest brain depth cue, namely retinal disparity. This can be achieved by capturing an image by horizontally separated cameras. Objects at different depths will be projected with different horizontal displacement on the left and right camera images. These images, when fed separately to either eye, leads to retinal disparity. Since the perception of depth is the single most important 3D imaging capability, an evaluation procedure is needed to quantify the depth capture characteristics. Evaluating depth capture characteristics subjectively is a very difficult task since the intended and/or unintended side effects from 3D image fusion (depth interpretation) by the brain are not immediately perceived by the observer, nor do such effects lend themselves easily to objective quantification. Objective evaluation of 3D camera depth characteristics is an important tool that can be used for "black box" characterization of 3D cameras. In this paper we propose a methodology to evaluate the 3D cameras' depth capture capabilities.
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Sergio R. Goma, Kalin Atanassov, and Vikas Ramachandra "Introducing the depth transfer curve for 3D capture system characterization", Proc. SPIE 7864, Three-Dimensional Imaging, Interaction, and Measurement, 78640E (27 January 2011); doi: 10.1117/12.879193; https://doi.org/10.1117/12.879193
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