Evaluation of a new Coarse-to-Fine Strategy for Fast Semi-Global Stereo Matching

Abstract

The paper considers semi-global stereo matching in the context of vision-based driver assistance systems. The need for real-time performance in this field requires a design change of the originally proposed method to run on current hardware. This paper proposes such a new design; the novel strategy first generates a disparity map from half-resolution input images. The result is then used as prior to restrict the disparity search space for full-resolution computation. This approach is compared to an SGM strategy as employed currently in a state-of-the-art real-time FPGA solution. Furthermore, trinocular stereo evaluation is performed on ten real-world traffic sequences with a total of 4,000 trinocular frames. An extension to the original evaluation methodology is proposed to resolve ambiguities and to incorporate disparity density in a statistically meaningful way. Evaluation results indicate that the novel SGM method is up to 40% faster when compared to the previous strategy. It returns denser disparity maps, and is also more accurate on evaluated traffic scenes.