A System for Capturing, Rendering and Multiplexing Images on Multi-view Autostereoscopic Display

Tan, Hock Soon; Xia, Jiazhi; He, Ying; Guan, Yunqing

doi:10.1109/cw.2010.27

Cited by 4 publications

(1 citation statement)

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“…Recently, the implementation of a variety of proofof-concept algorithms based on hardware acceleration approaches with GPUs consisting of a large number of cores have illustrated that GPUs can outperform and are more efficient than CPUs [20][21][22][23]. Further, it has been reported that GPUs can be ideal for executing arithmetically intensive scientific and engineering applications that contain a significant amount of parallelism [24].…”

Section: Introductionmentioning

confidence: 99%

Simultaneous reconstruction of multiple depth images without off-focus points in integral imaging using a graphics processing unit

Lee

Moon

2014

Appl. Opt.

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The reconstruction of multiple depth images with a ray back-propagation algorithm in three-dimensional (3D) computational integral imaging is computationally burdensome. Further, a reconstructed depth image consists of a focus and an off-focus area. Focus areas are 3D points on the surface of an object that are located at the reconstructed depth, while off-focus areas include 3D points in free-space that do not belong to any object surface in 3D space. Generally, without being removed, the presence of an off-focus area would adversely affect the high-level analysis of a 3D object, including its classification, recognition, and tracking. Here, we use a graphics processing unit (GPU) that supports parallel processing with multiple processors to simultaneously reconstruct multiple depth images using a lookup table containing the shifted values along the x and y directions for each elemental image in a given depth range. Moreover, each 3D point on a depth image can be measured by analyzing its statistical variance with its corresponding samples, which are captured by the two-dimensional (2D) elemental images. These statistical variances can be used to classify depth image pixels as either focus or off-focus points. At this stage, the measurement of focus and off-focus points in multiple depth images is also implemented in parallel on a GPU. Our proposed method is conducted based on the assumption that there is no occlusion of the 3D object during the capture stage of the integral imaging process. Experimental results have demonstrated that this method is capable of removing off-focus points in the reconstructed depth image. The results also showed that using a GPU to remove the off-focus points could greatly improve the overall computational speed compared with using a CPU.

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