A new combination of monocular and stereo cues for dense disparity estimation

Abstract

Disparity estimation is a popular and important topic in computer vision and robotics. Stereo vision is commonly done to complete the task, but most existing methods fail in textureless regions and utilize numerical methods to interpolate into these regions. Monocular features are usually ignored, which may contain helpful depth information. We proposed a novel method combining monocular and stereo cues to compute dense disparities from a pair of images. The whole image regions are categorized into reliable regions (textured and unoccluded) and unreliable regions (textureless or occluded). Stable and accurate disparities can be gained at reliable regions. Then for unreliable regions, we utilize k-means to find the most similar reliable regions in terms of monocular cues. Our method is simple and effective. Experiments show that our method can generate a more accurate disparity map than existing methods from images with large textureless regions, e.g. snow, icebergs.