Stratified Dense Matching for Stereopsis in Complex Scenes

Local joint image modeling in stereo matching brings more discriminable and stable matching features. Such features reduce the need for strong prior models (continuity) and thus algorithms that are less prone to false positive artefacts in general complex scenes can be applied. One of the principal quality factors in area-based dense stereo is the matching window shape. As it cannot be selected without having any initial matching hypothesis we propose a stratified matching approach. The window adapts to high-correlation structures in disparity space found in pre-matching which is then followed by final matching. In a rigorous ground-truth experiment we show that Stratified Dense Matching is able to increase matching density 3×, matching accuracy 1.8×, and occlusion boundary detection 2× as compared to a fixed-size rectangular windows algorithm. Performance on real outdoor complex scenes is also evaluated.