GPU-Accelerated Single Image Depth Estimation with Color-Filtered Aperture
Yueh-Teng Hsu, Chun‐Chieh Chen, Shu‐Ming Tseng · KSII Transactions on Internet and Information Systems · 2014
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.