A progressive framework for dense stereo matching

Abstract

A progressive framework is proposed for dense stereo matching to solve problems caused by weaktexture and occlusion in this paper. The main idea is that disparity is extracted progressively, from coarse to fine, from sparse to dense. First, a coarse disparity map is obtained by the segment-based pre-matching method, in which horizontal and vertical segment matching are performed in parallel and pre-matching results are merged to preserve more details. Second, disparity diffusion is performed to roughly estimate disparity values for miss-matched points. Third, a probabilistic approach is used for disparity refinement, taking into account stereo prior, image likehood and disparity smoothness. Experiments are made on the Middlebury benchmark to demostrate the effectiveness of the proposed algorithm.