Structured support vector machine with coarse-to-fine PatchMatch filtering for stereo matching.

In the past decades, a variety of learning-based algorithms have been emerged to try to explore a better solution for stereo matching by leveraging various machine learning algorithms. For enriching learning-based stereo matching algorithm's methodologies, we cast the disparity estimation as a regression problem by leveraging Structured Support Vector Machine (SSVM) in this paper. There are three categories of features have been extracted on account of disparity cues for training the SSVM. Particularly, one of the three feature is named as 'Coarse-to-Fine PatchMatch Filtering', which effectively exploits region and pixel disparity cues. For attaining region disparity cues, we adopt MeshStereo and MeshStereo with Cross-Scale algorithms; for attaining pixel disparity cues, PatchMatch and Cross-Scale PatchMatch stereo matching algorithms are utilized. Performance evaluations on Middlebury v.2 and v.3 stereo data sets demonstrate that the proposed algorithm reveals comparable accuracy with other challenging learning-based ones. It is worth pointing out that our proposal performs over several orders of magnitude faster than others on training time. [ABSTRACT FROM AUTHOR]