A Novel Two-Step Method for Stereo Vision Algorithm to Reduce Search Space

Stereo vision is a crucial algorithm in depth detection. By comparing images of a scene from two points, the relative position of objects is extracted. Human's vision system uses this relative shift between the left and right eyes to estimate the depth of information. The main goal of stereo vision is to determine the distance between objects in the scene or, in other words, to obtain depth information. This paper presents a two-step method to reduce the runtime and maintain accuracy of the stereo vision algorithm. Due to the data dependency, its implementation in parallel reduces performance. We have implemented this method for the different values of maximum disparity and window sizes. The simulation result shows that the proposed method is more than 6X faster than the common stereo vision. We have also implemented this method using Compute Unified Device Architecture (CUDA) on a Graphics Processing Unit (GPU), and we have shown that due to data dependency, this method does not work well on the Graphics Processing Unit.