A comparative study of fast dense stereo vision algorithms

With recent hardware advances, real-time dense stereo vision becomes increasingly feasible for general-purpose processors. This has important benefits for the intelligent vehicles domain, alleviating object segmentation problems when sensing complex, cluttered traffic scenes. In this paper, we presents a framework of real-time dense stereo vision algorithms that all based on a SIMD architecture. We distinguish different methodical components and examine their performance-speed trade-off. We furthermore compare the resulting algorithmic variations with an existing public source dynamic programming implementation from OpenCV and with the stereo methods discussed in Sharstein and Szeliski´s survey. Unlike the previous, we evaluate all stereo vision algorithms using realistically looking simulated data as well as real data, from complex urban traffic scenes.