Sub-pixel disparity search for binocular stereo vision

The idea of cost or similarity volume may date back to Marr and Poggio's cooperative algorithm. One of the challenging difficulties in applying their algorithm to recovering sub-pixel disparity is 1) how to take advantages of the edge location in the images for computing reliable cost between the corresponding sub-pixel coordinates. In addition, it should be noted that 2) accidental correspondences will produce many confusing sub-pixel disparity candidates with small cost. This study proposes a novel method where a reliable subpixel cost volume is computed though the combination of an interpolation method and an edge-preserving filtering method. In order to avoid confusing disparity candidates, the search range of the sub-pixel disparity will be limited according to the initial disparity which can be computed robustly but not so precisely in advance. All the procedures can be implemented in a highly-parallel style, therefore the proposed method seems promising for real-time applications.