Stereo Matching Algorithm Based on a Generalized Bilateral Filter Model
Stereo matching is a kernel problem in stereo vision systems. Stereo algorithms can be roughly classified into local and global approaches. Local algorithms use Winner-Take-All strategy, simply taking disparity level that minimizes the aggregation costs. In this paper, the authors present a local stereo matching algorithm with an adaptive cost aggregation strategy based on a generalized bilateral filter model. The range weight computation in the original bilateral filter is extended by the inner and outer weighted average processes. A pixel is assigned a high range weight to the central pixel not only if the patches of the two pixels are similar but also if the neighbouring patches around the two pixels are similar.