Adaptive Non-local Means for Cost Aggregation in a Local Disparity Estimation Algorithm

The overall method used for determining disparity in a stereo setup is a widely recognized framework consisting of four steps of cost space computation, cost aggregation, disparity selection, and post-processing. In this paper a cost aggregation approach for a typical local disparity estimation method is introduced. The method introduced is built on top of an existing method called Adaptive Support-Weight using this known framework. The introduced method improves Adaptive Support-Weight method by utilizing a larger amount of data inspired by the method of Non-Local Means. The extra data is handled in a way that tries to preserve the location of depth discontinuities in the final disparity map. Experimental results on Middlebury benchmark database show that the proposed method suffers from less artifacts compared to state-of-the-art disparity estimation methods.