Sparse recursive filtering for O(1) stereo matching

Recursive edge-aware filters have been proved to be one of the most efficient approaches for cost aggregation in stereo matching. However, disparity search space dependency, as a result of full search, is the bottle-neck of these local techniques that prevent further reduction in computation. In this paper, the cost aggregation and correspondence search problems are re-formulated to enable adaptive search for each pixel during recursive operations that provides significant reduction in computational complexity. In that manner, fixed number of disparity candidates are tested for each pixel, regardless of the search space, that are aggregated through sparse recursive filtering. Hierarchical approach is exploited to pick disparity candidates for each pixel. The experimental results show that the proposed approach has linear complexity with the image size and in practice it speeds up the recursive approaches almost four times with a marginal decrease in matching accuracy. Compared to the state-of-the-art techniques, hierarchical sparse recursive aggregation is possibly the fastest approach with a competitive accuracy based on Middlebury benchmarking.