Block based dense stereo matching using adaptive cost aggregation and limited disparity estimation

Abstract

Stereo matching is the key problem in many stereo vison based 3D applications. One of the factors make local stereo matching time-consuming is that every pixel has the same disparity range as the pre-set one, which should be larger than all possible disparities. An improper pre-set disparity range may lead to redundant computation for some pixels and inadequate computation for some others. In this paper, we propose an accurate and fast local stereo matching method, which employs fine disparity estimation and adaptive cost aggregation. The main contributions of our work include two parts. Firstly, we use phase-based correlation to estimate an initial disparity range for block center pixels. Secondly, we estimate a more limited disparity range for every in-block pixel according to its support weight to the block center pixel. Our disparity estimation and block matching techniques can not only reduce the disparity searching range for every pixel, but also can eliminate some pseudo match pairs. Four standard Middlebury stereo image pairs are tested to evaluate the performance of the proposed algorithm. Experimental results show that the proposed algorithm can reduce the matching time by 37.4% on average with relatively higher accuracy compared to traditional method.