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GPU-Accelerated Single Image Depth Estimation with Color-Filtered Aperture
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  • GPU-Accelerated Single Image Depth Estimation with Color-Filtered Aperture
  • GPU-Accelerated Single Image Depth Estimation with Color-Filtered Aperture
저자명
Hsu. Yueh-Teng,Chen. Chun-Chieh,Tseng. Shu-Ming
간행물명
KSII Transactions on internet and information systems : TIIS
권/호정보
2014년|8권 3호|pp.1058-1070 (13 pages)
발행정보
한국인터넷정보학회
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정기간행물|ENG|
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이 논문은 한국과학기술정보연구원과 논문 연계를 통해 무료로 제공되는 원문입니다.
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기타언어초록

There are two major ways to implement depth estimation, multiple image depth estimation and single image depth estimation, respectively. The former has a high hardware cost because it uses multiple cameras but it has a simple software algorithm. Conversely, the latter has a low hardware cost but the software algorithm is complex. One of the recent trends in this field is to make a system compact, or even portable, and to simplify the optical elements to be attached to the conventional camera. In this paper, we present an implementation of depth estimation with a single image using a graphics processing unit (GPU) in a desktop PC, and achieve real-time application via our evolutional algorithm and parallel processing technique, employing a compute shader. The methods greatly accelerate the compute-intensive implementation of depth estimation with a single view image from 0.003 frames per second (fps) (implemented in MATLAB) to 53 fps, which is almost twice the real-time standard of 30 fps. In the previous literature, to the best of our knowledge, no paper discusses the optimization of depth estimation using a single image, and the frame rate of our final result is better than that of previous studies using multiple images, whose frame rate is about 20fps.