Iterative refinement for real-time local stereo matching

We present a novel iterative refinement process to apply to any stereo matching algorithm. The quality of its disparity map output is increased using four rigorously defined refinement modules, which can be iterated multiple times: a disparity cross check, bitwise fast voting, invalid disparity handling, and median filtering. We apply our refinement process to our recently developed aggregation window method for stereo matching that combines two adaptive windows per pixel region [2]; one following the horizontal edges in the image, the other the vertical edges. Their combination defines the final aggregation window shape that closely follows all object edges and thereby achieves increased hypothesis confidence. We demonstrate that the iterative disparity refinement has a large effect on the overall quality, especially around occluded areas, and tends to converge to a final solution. We perform a quantitative evaluation on various Middlebury datasets. Our whole disparity estimation process supports efficient GPU implementation to facilitate scalability and real-time performance.