As‐global‐as‐possible stereo matching with adaptive smoothness prior

Abstract

More global matching (MGM) overcomes the limitation of one-dimensional scanline optimisation in semi-global matching (SGM). Nevertheless, the possible weaknesses of the MGM algorithm are as follows: (i) only two directions are considered for each image traversal direction, which may lead to massive mismatches; (ii) disparity estimation around the object boundaries usually performs terrible since the smoothness term is designed independent of the image prior. In this research, the authors consider all of the four directions for each image traversal direction through a novel model. Besides utilising the prior of neighboured pixels' correlation, adaptive smoothness terms are modelled and augmented into the energy function. These contributions encourage ‘as-global-as-possible (AGAP)’. More importantly, different from the recent works in which the aggregated algorithms have been conducted as the data term of an energy function, conversely, the authors make the energy function as a part of cost aggregation framework. Performance evaluations on Middlebury v.2 and v.3 stereo data sets demonstrate that the proposed AGAP outperforms other four most challenging stereo matching algorithms, and also performs better on Microsoft i2i stereo videos. In addition, under various strategies of parallelisation, the presented AGAP shows a near real-time execution time.