Near-real-time stereo matching with slanted surface modeling and sub-pixel accuracy
This paper presents a new stereo matching algorithm which takes into consideration surface orientation at the per-pixel level. Two disparity calculation passes are used. The first pass assumes that surfaces in the scene are fronto-parallel and generates an initial disparity map, from which the disparity plane orientations of all pixels are estimated and refined. In the second pass, the matching costs for different pixels are aggregated along the estimated disparity plane orientations using adaptive support weights, where the support weights of neighboring pixels are calculated using a combination of four terms: a spatial proximity term, a color similarity term, a disparity similarity term, and an occlusion handling term. The disparity search space is quantized at sub-pixel level to improve the accuracy of the disparity results. The algorithm is designed for parallel execution on Graphics Processing Units (GPUs) for near-real-time processing speed. The evaluation using Middlebury benchmark shows that the presented approach outperforms existing real-time and near-real-time algorithms in terms of subpixel level accuracy.
real-time stereo adaptive-weight cost aggregation gpu computing