Real-time local stereo via edge-aware disparity propagation

This letter presents a novel method for real-time local stereo matching. Different from previous methods which have spent many efforts on cost aggregation, the proposed method re-solves the stereo problem by propagating disparities in the cost domain. It is started by pre-detecting the disparity priors, on which a new cost volume is built for disparity assignment. Then the reliable disparities are propagated via filtering on this cost volume. Specially, a new O ( 1 ) geodesic filter is proposed and demonstrated effective for the task of edge-aware disparity propagation. As can be expected, the proposed framework is highly efficient, due to leaving double aggregation on left-right views, as well as costly post-processing steps, out of account. Moreover, by properly designing a quadric cost function, our method could be extended to good sub-pixel accuracy with a simple quadratic polynomial interpolation. Quantitative evaluation shows that it outperforms all the other local methods both in terms of accuracy and speed on Middlebury benchmark. It ranks 8th out of over 150 submissions if sub-pixel precision is considered, and the average runtime is only 9ms on a NVIDIA GeForce GTX 580GPU.