Improving stereo reconstruction by sub-pixel correction using histogram matching

Abstract

The accuracy of stereo matching algorithms is one of the key aspects in autonomous driving nowadays. In case of large distances, sub-pixel accurate solutions are required, especially for algorithms in discrete settings. It has been previously shown that a strong correlation between the matching algorithm and the sub-pixel interpolation method exists, and there are ways to determine it. Unfortunately all methodologies presented so far are laborious and time-consuming. We present here a novel sub-pixel disparity correction technique based on applying histogram matching through the use of generated Look-up Tables (LUTs). Our method is flexible, fast and produces more accurate results than previous solutions in the discrete domain. Although we show the improvements over the Semi-Global matching algorithm, it can be adapted to other matching algorithms that preserve constant misalignment for any kind of 3D scenarios. The proposed method was tested on multiple systems and datasets (Synthetic images, Traffic scenes, Middlebury images, KITTI images) and we show that we can find LUTs that outperform the accuracy of previous solutions on all these sets. The histogram matching procedure lacks in complexity and results indicate a strict dependency of a particular LUT to the underlying stereo matching and the stereo vision system, but not on the image composition.