Local stereo matching with adaptive shape support window based cost aggregation

Cost aggregation is the most important step in a local stereo algorithm. In this work, a novel local stereo-matching algorithm with a cost-aggregation method based on adaptive shape support window (ASSW) is proposed. First, we compute the initial cost volume, which uses both absolute intensity difference and gradient similarity to measure dissimilarity. Second, we apply an ASSW-based cost-aggregation method to get the aggregated cost within the support window. There are two main parts: at first we construct a local support skeleton anchoring each pixel with four varying arm lengths decided on color similarity; as a result, the support window integral of multiple horizontal segments spanned by pixels in the neighboring vertical is established. Then we utilize extended implementation of guided filter to aggregate cost volume within the ASSW, which has better edge-preserving smoothing property than bilateral filter independent of the filtering kernel size. In this way, the number of bad pixels located in the incorrect depth regions can be effectively reduced through finding optimal support windows with an arbitrary shape and size adaptively. Finally, the initial disparity value of each pixel is selected using winner takes all optimization and post processing symmetrically, considering both the reference and the target image, is adopted. The experimental results demonstrate that the proposed algorithm achieves outstanding matching performance compared with other existing local algorithms on the Middlebury stereo benchmark, especially in depth discontinuities and piecewise smooth regions.