Abstract
AbstractStereo matching is a kernel problem in the field of stereo vision. An adaptive cost aggregation strategy based on a generalized bilateral filter model is proposed. The strategy extends range weights of the original bilateral filter 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 neighboring patches around the two pixels are similar. The final range weights could more accurately reflect the similarities of relevant two pixels. Different cost aggregation methods can be derived from the model by modifying parameters. Experimental results compared with the other state-of-the-art cost aggregation methods demonstrate the effectiveness of our proposed cost aggregation strategy.
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