Quality enhancement and GPU acceleration for a full-color holographic system using a relocated point cloud gridding method

Yu Zhao; Ki-Chul Kwon; Munkh-Uchral Erdenebat; Md-Sifatul Islam; Seok-Hee Jeon; Nam Kim

doi:10.1364/AO.57.004253

Applied Optics
Vol. 57,
Issue 15,
pp. 4253-4262
(2018)
•https://doi.org/10.1364/AO.57.004253

Quality enhancement and GPU acceleration for a full-color holographic system using a relocated point cloud gridding method

Yu Zhao, Ki-Chul Kwon, Munkh-Uchral Erdenebat, Md-Sifatul Islam, Seok-Hee Jeon, and Nam Kim

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Yu Zhao, Ki-Chul Kwon, Munkh-Uchral Erdenebat, Md-Sifatul Islam, Seok-Hee Jeon, and Nam Kim, "Quality enhancement and GPU acceleration for a full-color holographic system using a relocated point cloud gridding method," Appl. Opt. 57, 4253-4262 (2018)

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Abstract

The calculation of realistic full-color holographic displays is hindered by the high computational cost. Previously, we suggested a point cloud gridding (PCG) method to calculate monochrome holograms of real objects. In this research, a relocated point cloud gridding (R-PCG) method is proposed to enhance the reconstruction quality and accelerate the calculation speed in GPU for a full-color holographic system. We use a depth camera to acquire depth and color information from the real scene then reconstruct the point cloud model virtually. The R-PCG method allows us to classify groups of object points with the same depth values into grids in the red, green, and blue (RGB) channels. Computer-generated holograms (CGHs) are obtained by applying a fast Fourier transform (FFT) diffraction calculation to the grids. The feasibility of the R-PCG method is confirmed by numerical and optical reconstruction.

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Parameter		Value
Model		F200
Resolution of the color stream		$1920 \times 1080 / 30 fps$
Resolution of the depth stream		$640 \times 480 / 30 fps$
Range of detection		$0.2 \sim 1.5 m$
Angle of depth	Horizontal	70°
Angle of depth	Vertical	46°

Parameter	Value
CGH size	$30 \times 30 mm$
CGH pixel size	7.4 μm
RGB wavelengths	633,532 and 473 nm
Distance from object to CGH	300 mm

Object			CPU Running Time			GPU Running Time
Name	Point Number	Depth Grids Number	WRP	PCG	R-PCG	WRP	PCG	R-PCG
Toy	20k	50	829.268	13.315	9.638	1.770	0.192	0.143
Two toys	44k	107	1543.851	22.315	14.336	2.755	0.684	0.570
Cactus, daisy, and chicken	88k	128	2919.880	28.898	18.650	7.746	1.013	0.801
Person	70k	349	2318.355	49.301	35.017	7.603	4.213	3.572

Object			CPU Running Time			GPU Running Time
Name	Point Number	Depth Grids Number	WRP	PCG	R-PCG	WRP	PCG	R-PCG
Toy	20k	50	2339.174	63.270	40.549	4.987	0.401	0.257
Two toys	44k	107	5977.015	140.173	86.341	19.551	5.956	5.272
Cactus, daisy, and chicken	88k	128	14829.268	187.064	108.650	49.270	6.435	5.811
Person	70k	349	12817.361	397.903	255.017	47.691	22.835	14.635

Parameter		Value
Model		F200
Resolution of the color stream		$1920 \times 1080 / 30 fps$
Resolution of the depth stream		$640 \times 480 / 30 fps$
Range of detection		$0.2 \sim 1.5 m$
Angle of depth	Horizontal	70°
Angle of depth	Vertical	46°

Quality enhancement and GPU acceleration for a full-color holographic system using a relocated point cloud gridding method

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Abstract

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Tables (4)

Equations (8)

Applied Optics